Athero-Express Biobank Study – IL6 and MCP1 plaque levels.
An ‘Athero-Express Biobank Study’ project
Sander W. van der Laan, PhD | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans
2020-07-07
Preparation
Clean the environment.
Set locations, and the working directory.
A package-installation function.
Load those packages.
We will create a datestamp and define the Utrecht Science Park Colour Scheme.
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
if (nrow(summary(fit)$coefficients) == 1) {
output = c(DATASET, x_name, y, rep(NA,8))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data.\n\n")
effectsize = summary(fit)$coefficients[2,1]
SE = summary(fit)$coefficients[2,2]
OReffect = exp(summary(fit)$coefficients[2,1])
CI_low = exp(effectsize - 1.96 * SE)
CI_up = exp(effectsize + 1.96 * SE)
tvalue = summary(fit)$coefficients[2,3]
pvalue = summary(fit)$coefficients[2,4]
R = summary(fit)$r.squared
R.adj = summary(fit)$adj.r.squared
sample_size = nrow(model.frame(fit))
AE_N = AEDB.CEA.samplesize
Perc_Miss = 100 - ((sample_size * 100)/AE_N)
output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
if (verbose == TRUE) {
cat("We have collected the following and summarize it in an object:\n")
cat("Dataset...................:", DATASET, "\n")
cat("Score/Exposure/biomarker..:", x_name, "\n")
cat("Trait/outcome.............:", y, "\n")
cat("Effect size...............:", round(effectsize, 6), "\n")
cat("Standard error............:", round(SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
cat("T-value...................:", round(tvalue, 6), "\n")
cat("P-value...................:", signif(pvalue, 8), "\n")
cat("R^2.......................:", round(R, 6), "\n")
cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
cat("Sample size of AE DB......:", AE_N, "\n")
cat("Sample size of model......:", sample_size, "\n")
cat("Missing data %............:", round(Perc_Miss, 6), "\n")
} else {
cat("Collecting data in summary object.\n")
}
}
return(output)
print(output)
}
GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
if (nrow(summary(fit)$coefficients) == 1) {
output = c(DATASET, x_name, y, rep(NA,9))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data...\n")
effectsize = summary(fit)$coefficients[2,1]
SE = summary(fit)$coefficients[2,2]
OReffect = exp(summary(fit)$coefficients[2,1])
CI_low = exp(effectsize - 1.96 * SE)
CI_up = exp(effectsize + 1.96 * SE)
zvalue = summary(fit)$coefficients[2,3]
pvalue = summary(fit)$coefficients[2,4]
dev <- fit$deviance
nullDev <- fit$null.deviance
modelN <- length(fit$fitted.values)
R.l <- 1 - dev / nullDev
R.cs <- 1 - exp(-(nullDev - dev) / modelN)
R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
sample_size = nrow(model.frame(fit))
AE_N = AEDB.CEA.samplesize
Perc_Miss = 100 - ((sample_size * 100)/AE_N)
output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
if (verbose == TRUE) {
cat("We have collected the following and summarize it in an object:\n")
cat("Dataset...................:", DATASET, "\n")
cat("Score/Exposure/biomarker..:", x_name, "\n")
cat("Trait/outcome.............:", y, "\n")
cat("Effect size...............:", round(effectsize, 6), "\n")
cat("Standard error............:", round(SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
cat("Z-value...................:", round(zvalue, 6), "\n")
cat("P-value...................:", signif(pvalue, 8), "\n")
cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
cat("Sample size of AE DB......:", AE_N, "\n")
cat("Sample size of model......:", sample_size, "\n")
cat("Missing data %............:", round(Perc_Miss, 6), "\n")
} else {
cat("Collecting data in summary object.\n")
}
}
return(output)
print(output)
}Background
Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b). While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.
Objectives
Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.
Methods
Blood
OLINK-platform
- IL6: Interleukin 6. Entrez Gene: 3569. OLINK, plasma
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. OLINK, plasma
THESE DATA ARE NOT AVAILABLE YET
Plaque
Luminex-platform, measured by Luminex
- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug]. Measured in two experiments, variables
MCP1andMCP1_pg_ug_2015. The latter was corrected for plaque total protein concentration. - IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug].
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug].
FACS platform
- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]
Loading data
Clinical data
Loading Athero-Express clinical data.
require(haven)
# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDBraw <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))
head(AEDBraw)Plaque protein data
Loading Athero-Express plaque protein measurements from 2015.
library(openxlsx)
AEDB_Protein_2015 <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_Proteins/Cytokines_and_chemokines_2015/20200629_MPCF015-0024.xlsx"), sheet = "for_SPSS_R")
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "SampleID"] <- "STUDY_NUMBER"
head(AEDB_Protein_2015)NAPlasma protein data
Loading Athero-Express plasma protein measurements from 2019/2020 as measured using OLINK.
library(openxlsx)
AEDB_PlasmaProtein_OLINK_CVD2raw <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "CVD2_forR")
AEDB_PlasmaProtein_OLINK_CVD3raw <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "CVD3_forR")
AEDB_PlasmaProtein_OLINK_CMraw <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "CM_forR")
AEDB_PlasmaProtein_OLINK_ProteinInfo <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "ProteinInfo")
AEDB_PlasmaProtein_OLINK_CVD2 <- AEDB_PlasmaProtein_OLINK_CVD2raw %>% filter(QC_Warning_CVD2 == "Pass")
AEDB_PlasmaProtein_OLINK_CVD3 <- AEDB_PlasmaProtein_OLINK_CVD3raw %>% filter(QC_Warning_CVD3 == "Pass")
AEDB_PlasmaProtein_OLINK_CM <- AEDB_PlasmaProtein_OLINK_CMraw %>% filter(QC_Warning_CM == "Pass")
table(AEDB_PlasmaProtein_OLINK_CVD2raw$QC_Warning_CVD2)
Pass Warning
690 10 table(AEDB_PlasmaProtein_OLINK_CVD2$QC_Warning_CVD2)
Pass
690 table(AEDB_PlasmaProtein_OLINK_CVD3raw$QC_Warning_CVD3)
Pass
699 table(AEDB_PlasmaProtein_OLINK_CVD3$QC_Warning_CVD3)
Pass
699 table(AEDB_PlasmaProtein_OLINK_CMraw$QC_Warning_CM)
Pass Warning
691 9 table(AEDB_PlasmaProtein_OLINK_CM$QC_Warning_CM)
Pass
691 AEDB_PlasmaProtein_OLINK_CVD2$Plate_ID <- NULL
AEDB_PlasmaProtein_OLINK_CVD3$Plate_ID <- NULL
AEDB_PlasmaProtein_OLINK_CVD2$Order <- NULL
AEDB_PlasmaProtein_OLINK_CVD3$Order <- NULL
AEDB_PlasmaProtein_OLINK_CM$Order <- NULL
temp <- merge(AEDB_PlasmaProtein_OLINK_CVD2, AEDB_PlasmaProtein_OLINK_CVD3, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER",
sort = FALSE, all.x = TRUE)
AEDB_PlasmaProtein_OLINK <- merge(temp, AEDB_PlasmaProtein_OLINK_CM, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER",
sort = FALSE, all.x = TRUE)
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_cardiometabolic_plt1_29-10-19"] <- "plate 1"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt2"] <- "plate 2"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt3"] <- "plate 3"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt4"] <- "plate 4"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt5"] <- "plate 5"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_pl6"] <- "plate 6"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "SMART_CM_plt10"] <- "plate 10"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "SMART_plt11_CM"] <- "plate 11"
olink_proteins <- c("BMP6", "ANGPT1", "ADM", "CD40L", "SLAMF7", "PGF", "ADAMTS13", "BOC", "IL4RA", "SRC", "IL1ra", "IL6", "TNFRSF10A", "STK4", "IDUA",
"TNFRSF11A", "PAR1", "TRAILR2", "PRSS27", "TIE2", "TF", "IL1RL2", "PDGF_subunit_B", "IL27", "IL17D", "CXCL1", "LOX1", "Gal9", "GIF", "SCF",
"IL18", "FGF21", "PIgR", "RAGE", "SOD2", "CTRC", "FGF23", "SPON2", "GH", "FS", "GLO1", "CD84", "PAPPA", "SERPINA12", "REN", "DECR1",
"MERTK", "KIM1", "THBS2", "TM", "VSIG2", "AMBP", "PRELP", "HO1", "XCL1", "IL16", "SORT1", "CEACAM8", "PTX3", "PSGL1", "CCL17", "CCL3",
"MMP7", "IgG_Fc_receptor_IIb", "ITGB1BP2", "DCN", "Dkk1", "LPL", "PRSS8", "AGRP", "HBEGF", "GDF2", "FABP2", "THPO", "MARCO", "GT", "BNP",
"MMP12", "ACE2", "PDL2", "CTSL1", "hOSCAR", "TNFRSF13B", "TGM2", "LEP", "CA5A", "HSP_27", "CD4", "NEMO", "VEGFD", "PARP1", "HAOX1",
"TNFRSF14", "LDL_receptor", "ITGB2", "IL17RA", "TNFR2", "MMP9", "EPHB4", "IL2RA", "OPG", "ALCAM", "TFF3", "SELP", "CSTB", "MCP1", "CD163",
"Gal3", "GRN", "NTproBNP", "BLM_hydrolase", "PLC", "LTBR", "Notch_3", "TIMP4", "CNTN1", "CDH5", "TLT2", "FABP4", "TFPI", "PAI", "CCL24",
"TR", "TNFRSF10C", "GDF15", "SELE", "AZU1", "DLK1", "SPON1", "MPO", "CXCL16", "IL6RA", "RETN", "IGFBP1", "CHIT1", "TRAP", "GP6", "PSPD",
"PI3", "EpCAM", "APN", "AXL", "IL1RT1", "MMP2", "FAS", "MB", "TNFSF13B", "PRTN3", "PCSK9", "UPAR", "OPN", "CTSD", "PGLYRP1", "CPA1", "JAMA",
"Gal4", "IL1RT2", "SHPS1", "CCL15", "CASP3", "uPA", "CPB1", "CHI3L1", "ST2", "tPA", "SCGB3A2", "EGFR", "IGFBP7", "CD93", "IL18BP", "COL1A1",
"PON3", "CTSZ", "MMP3", "RARRES2", "ICAM2", "KLK6", "PDGF_subunit_A", "TNFR1", "IGFBP2", "vWF", "PECAM1", "MEPE", "CCL16", "PRCP", "CA1",
"ICAM1", "CHL1", "TGFBI", "ENG", "PLTP", "SERPINA7", "IGFBP3", "CR2", "SERPINA5", "FCGR3B", "IGFBP6", "CDH1", "CCL5", "CCL14", "GNLY",
"NOTCH1", "PAM", "PROC", "CST3", "NCAM1", "PCOLCE", "LILRB1", "MET", "LTBP2", "IL7R", "VCAM1", "SELL", "F11", "COMP", "CA4", "PTPRS",
"MBL2", "TIMP1", "ANGPTL3", "REG3A", "SOD1", "CD46", "ITGAM", "TNC", "NID1", "CFHR5", "SPARCL1", "PLXNB2", "MEGF9", "ANG", "ST6GAL1",
"DPP4", "REG1A", "QPCT", "FCN2", "FETUB", "CES1", "CRTAC1", "TCN2", "PRSS2", "ICAM3", "SAA4", "CNDP1", "FCGR2A", "NRP1", "EFEMP1", "TIMD4",
"FAP", "TIE1", "THBS4", "F7", "GP1BA", "LYVE1", "CA3", "TGFBR3", "DEFA1", "CD59", "APOM", "OSMR", "LILRB2", "UMOD", "CCL18", "COL18A1",
"LCN2", "KIT", "C1QTNF1", "AOC3", "GAS6", "IGLC2", "PLA2G7", "TNXB", "MFAP5", "VASN", "LILRB5", "C2")
length(olink_proteins)[1] 276olink_proteins_rank = unlist(lapply(olink_proteins, paste0, "_rankNorm"))
olink_proteins_short <- c("MCP1")
olink_proteins_short_rank <- unlist(lapply(olink_proteins_short, paste0, "_rankNorm"))
rm(temp)
Inspect OLINK data
We know that the proteins are not normally distributed and therefore we will standardise them as follows:
z = ( x - μ ) / σ
Where for each sample, x equals the value of the variable, μ (mu) equals the mean of x, and σ (sigma) equals the standard deviation of x.
for(PROTEIN in 1:length(olink_proteins_short)){
# AEDB_PlasmaProtein_OLINK$Z <- NULL
var.temp.z = olink_proteins_short_rank[PROTEIN]
var.temp = olink_proteins_short[PROTEIN]
cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.z,".\n"))
cat(paste0("* changing ", var.temp, " to numeric.\n"))
AEDB_PlasmaProtein_OLINK <- AEDB_PlasmaProtein_OLINK %>%
mutate_each_(funs(as.numeric), olink_proteins_short)
cat(paste0("* standardising ", var.temp,
" (mean: ",round(mean(!is.na(AEDB_PlasmaProtein_OLINK[,var.temp])), digits = 6),
", n = ",sum(!is.na(AEDB_PlasmaProtein_OLINK[,var.temp])),").\n"))
AEDB_PlasmaProtein_OLINK <- AEDB_PlasmaProtein_OLINK %>%
mutate_at(vars(var.temp),
list(Z = ~ (AEDB_PlasmaProtein_OLINK[,var.temp] - mean(AEDB_PlasmaProtein_OLINK[,var.temp], na.rm = TRUE))/sd(AEDB_PlasmaProtein_OLINK[,var.temp], na.rm = TRUE))
)
# str(AEDB_PlasmaProtein_OLINK$Z)
cat(paste0("* renaming Z to ", var.temp.z,".\n"))
AEDB_PlasmaProtein_OLINK[,var.temp.z] <- NULL
names(AEDB_PlasmaProtein_OLINK)[names(AEDB_PlasmaProtein_OLINK) == "Z"] <- var.temp.z
}
Selecting MCP1 and standardising: MCP1_rankNorm.
* changing MCP1 to numeric.
* standardising MCP1 (mean: 0.997101, n = 688).
* renaming Z to MCP1_rankNorm.rm(var.temp, var.temp.z)Here we summarize some of these data in the subset of genetic data that passed QC.
for(PROTEIN in 1:length(olink_proteins_short)){
var.temp.z = olink_proteins_short_rank[PROTEIN]
var.temp = olink_proteins_short[PROTEIN]
cat(paste0("\nSummarising data for ",var.temp," [AU]; n = ",sum(!is.na(AEDB_PlasmaProtein_OLINK[,var.temp])),".\n"))
print(summary(AEDB_PlasmaProtein_OLINK[,var.temp]))
print(summary(AEDB_PlasmaProtein_OLINK[,var.temp.z]))
}
Summarising data for MCP1 [AU]; n = 688.
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.6649 3.3221 3.5619 3.6085 3.8117 12.2641 2
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
-5.08789 -0.49497 -0.08056 0.00000 0.35114 14.96088 2 rm(var.temp, var.temp.z, PROTEIN)
require("ggpubr")
require("ggsci")
# mypal = pal_npg("nrc", alpha = 0.7)(9)
# mypal
# ## [1] "#E64B35B2" "#4DBBD5B2" "#00A087B2" "#3C5488B2" "#F39B7FB2" "#8491B4B2"
# ## [7] "#91D1C2B2" "#DC0000B2" "#7E6148B2"
# library("scales")
# show_col(mypal)
for(PROTEIN in 1:length(olink_proteins_short)){
# metabolite_unit = ucorbioNMRDataDictionary[ucorbioNMRDataDictionary$Metabolite_NMR == NMRtargets[METABOLITE], "Concentration_NMR"]
cat(paste0("\nProcessing metabolite [ ",olink_proteins_short[PROTEIN]," (AU)].\n"))
var.temp = olink_proteins_short[PROTEIN]
var.temp.z = paste0(olink_proteins_short[PROTEIN],"_rankNorm")
dt.temp <- subset(AEDB_PlasmaProtein_OLINK, select = c("STUDY_NUMBER", var.temp, var.temp.z, "Plate_ID"))
dt.temp[,2] <- as.numeric(dt.temp[,2])
p1 <- ggpubr::gghistogram(dt.temp %>% filter(!is.na(Plate_ID)),
x = var.temp,
y = "..count..",
color = "#4DBBD5B2", fill = "#4DBBD5B2",
# palette = "npg",
rug = TRUE,
add = "mean",
xlab = paste0(var.temp," [AU]."),
ggtheme = theme_minimal())
my_comparisons <- list( c("plate 1", "plate 2"),
c("plate 1", "plate 3"),
c("plate 1", "plate 4"),
c("plate 1", "plate 5"),
c("plate 1", "plate 6"),
c("plate 1", "plate 10"),
c("plate 1", "plate 11") )
p2 <- ggpubr::ggboxplot(data = dt.temp %>% filter(!is.na(Plate_ID)),
x = "Plate_ID",
y = var.temp.z,
color = "Plate_ID",
palette = "npg",
add = c("mean", "jitter"),
# error.plot = "errorbar",
xlab = "plates used",
ylab = paste0(var.temp.z," [AU]."),
# ylim = c(0,4),
ggtheme = theme_minimal()) #+
# stat_compare_means(method = "anova") #+ # Add global p-value
# stat_compare_means(comparisons = my_comparisons) + # Add pairwise comparisons p-value
# stat_compare_means(label = "p.signif", method = "t.test", ref.group = "plate 1")
p3 <- ggpubr::gghistogram(dt.temp %>% filter(!is.na(Plate_ID)),
x = var.temp.z,
y = "..count..",
color = "#91D1C2B2", fill = "#91D1C2B2",
# palette = "npg",
rug = TRUE,
add = "mean",
xlab = paste0(var.temp.z," [AU]."),
ggtheme = theme_minimal())
require(patchwork)
# p4 <- ((p1 / p3 ) | (p2))
p4 <- ggpar(p1, legend = "" ) / ggpar(p2 + rotate_x_text(45), legend = "") | ggpar(p3, legend = "right")
print(p4)
ggsave(filename = paste0(QC_loc, "/",Today,".",PROJECTNAME,".OLINK.",var.temp,".png"),
plot = p4, device = "png", width = 20, height = 20)
}
Processing metabolite [ MCP1 (AU)].Using `bins = 30` by default. Pick better value with the argument `bins`.geom_vline(): Ignoring `mapping` because `xintercept` was provided.geom_vline(): Ignoring `data` because `xintercept` was provided.`fun.y` is deprecated. Use `fun` instead.`fun.ymin` is deprecated. Use `fun.min` instead.`fun.ymax` is deprecated. Use `fun.max` instead.Using `bins = 30` by default. Pick better value with the argument `bins`.geom_vline(): Ignoring `mapping` because `xintercept` was provided.geom_vline(): Ignoring `data` because `xintercept` was provided.
# rm(my_comparisons,
# p1, p2, p3, p4,
# var.temp, var.temp.z, dt.temp, PROTEIN)
Merging protein data
We will merge these measurements to the AEDB for comparing pg/ug vs. pg/mL measurements of MCP1 - also in relation to plaque phenotypes. In addition we have more information the experiment and can correct for this.
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6_pg_ml"] <- "IL6_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6R_pg_ml"] <- "IL6R_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL8_pg_ml"] <- "IL8_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCP1_pg_ml"] <- "MCP1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "RANTES_pg_ml"] <- "RANTES_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "PAI1_pg_ml"] <- "PAI1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCSF_pg_ml"] <- "MCSF_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Adiponectin_ng_ml"] <- "Adiponectin_ng_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Segment_isolated_Tris"] <- "Segment_isolated_Tris_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Tris_protein_conc_ug_ml"] <- "Tris_protein_conc_ug_ml_2015"
temp <- subset(AEDB_Protein_2015, select = c("STUDY_NUMBER", "IL6_pg_ml_2015", "IL6R_pg_ml_2015", "IL8_pg_ml_2015", "MCP1_pg_ml_2015", "RANTES_pg_ml_2015", "PAI1_pg_ml_2015", "MCSF_pg_ml_2015", "Adiponectin_ng_ml_2015", "Segment_isolated_Tris_2015", "Tris_protein_conc_ug_ml_2015"))
temp2 <- subset(AEDB_PlasmaProtein_OLINK, select = c("STUDY_NUMBER", "MCP1", "MCP1_rankNorm", "Plate_ID"))
names(temp2)[names(temp2) == "MCP1"] <- "MCP1_plasma_olink"
names(temp2)[names(temp2) == "MCP1_rankNorm"] <- "MCP1_plasma_olink_rankNorm"
names(temp2)[names(temp2) == "Plate_ID"] <- "PlateID_plasma_olink"
AEDBraw2 <- merge(AEDBraw, temp, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
all.x = TRUE)
AEDB <- merge(AEDBraw2, temp2, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
all.x = TRUE)
rm(temp, temp2, AEDBraw2)
temp <- subset(AEDB, select = c("STUDY_NUMBER", "MCP1", "MCP1_pg_ug_2015", "MCP1_pg_ml_2015", "Segment_isolated_Tris_2015",
"MCP1_plasma_olink", "MCP1_plasma_olink_rankNorm", "PlateID_plasma_olink"))
dim(temp)[1] 3793 8head(temp)rm(temp) Examine AEDB
We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is.
There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/.
AEDB %>% sjPlot::view_df(show.type = TRUE,
show.frq = TRUE,
show.prc = TRUE,
show.na = TRUE,
max.len = TRUE,
wrap.labels = 20,
verbose = FALSE,
use.viewer = FALSE,
file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) Following 3 variables have only missing values and are not shown:yearpsy5 [326], yearchol3 [347], yearablo3 [419]Fixing and creating variables
We need to be very strict in defining symptoms. Therefore we will fix a new variable that groups symptoms at inclusion.
Coding of symptoms is as follows:
- missing -999
- Asymptomatic 0
- TIA 1
- minor stroke 2
- Major stroke 3
- Amaurosis fugax 4
- Four vessel disease 5
- Vertebrobasilary TIA 7
- Retinal infarction 8
- Symptomatic, but aspecific symtoms 9
- Contralateral symptomatic occlusion 10
- retinal infarction 11
- armclaudication due to occlusion subclavian artery, CEA needed for bypass 12
- retinal infarction + TIAs 13
- Ocular ischemic syndrome 14
- ischemisch glaucoom 15
- subclavian steal syndrome 16
- TGA 17
We will group as follows in Symptoms.5G:
- Asymptomatic > 0
- TIA > 1, 7, 13
- Stroke > 2, 3
- Ocular > 4, 14, 15
- Retinal infarction > 8, 11
- Other > 5, 9, 10, 12, 16, 17
We will also group as follows in AsymptSympt:
- Asymptomatic > 0
- TIA > 1, 7, 13 + Stroke > 2, 3
- Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
We will also group as follows in AsymptSympt2G:
- Asymptomatic > 0
- TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17
# Fix symptoms
attach(AEDB)
AEDB$sympt[is.na(AEDB$sympt)] <- -999
# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"
# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"
# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"
detach(AEDB)
# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
#
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")
Asymptomatic Ocular Other Retinal infarction Stroke TIA <NA>
Asymptomatic 333 0 0 0 0 0 0
Symptomatic 0 417 119 43 733 1045 0
<NA> 0 0 0 0 0 0 1103# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
#
# rm(AEDB.temp)
We will also fix the plaquephenotypes variable.
Coding of symptoms is as follows:
- missing -999
- not relevant -888
- fibrous 1
- fibroatheromatous 2
- atheromatous 3
# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)
table(AEDB$OverallPlaquePhenotype)
atheromatous fibroatheromatous fibrous
550 843 1439 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the diabetes status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)
table(AEDB$DM.composite)
0 1
2766 985 table(AEDB$DiabetesStatus)
Control (no Diabetes Dx/Med) Diabetes
2766 985 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the smoking status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire.
diet801: are you a smoker?diet802: did you smoke in the past?
We already have some variables indicating smoking status:
SmokingReported: patient has reported to smoke.SmokingYearOR: smoking in the year of surgery?SmokerCurrent: currently smoking?
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)
# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
#
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))
cat("\nFixing smoking status.\n")
Fixing smoking status.attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)
cat("\n* Current smoking status.\n")
* Current smoking status.table(AEDB$SmokerCurrent,
useNA = "ifany",
dnn = c("Current smoker"))Current smoker
no data available/missing no yes <NA>
0 2364 1310 119 cat("\n* Updated smoking status.\n")
* Updated smoking status.table(AEDB$SmokerStatus,
useNA = "ifany",
dnn = c("Updated smoking status"))Updated smoking status
Current smoker Ex-smoker Never smoked <NA>
1310 1814 389 280 cat("\n* Comparing to 'SmokerCurrent'.\n")
* Comparing to 'SmokerCurrent'.table(AEDB$SmokerStatus, AEDB$SmokerCurrent,
useNA = "ifany",
dnn = c("Updated smoking status", "Current smoker")) Current smoker
Updated smoking status no data available/missing no yes <NA>
Current smoker 0 0 1310 0
Ex-smoker 0 1814 0 0
Never smoked 0 389 0 0
<NA> 0 161 0 119# AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix the alcohol status variable.
# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)
table(AEDB$AlcoholUse)
No Yes
1238 2346 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on CAD_history, Stroke_history, and Peripheral.interv
# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)
table(AEDB$CAD_history)
0 1
2432 1285 table(AEDB$Stroke_history)
0 1
2764 948 table(AEDB$Peripheral.interv)
0 1
2581 1099 table(AEDB$MedHx_CVD)
No yes
1310 2476 # AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
#
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
#
# rm(AEDB.temp)
Athero-Express Biobank Study
Baseline characteristics
We are interested in the following variables at baseline.
- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
- Never smokers
- Ex-smokers
- Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
- Asymptomatic
- Amaurosis fugax
- TIA
- Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments
MCP1, andMCP1_pg_ug_2015)
NOT AVAILABLE YET - MCP-1 plasma levels (pg/mL) (OLINK based)
cat("===========================================================================================\n")===========================================================================================cat("CREATE BASELINE TABLE\n")CREATE BASELINE TABLE# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
"Age", "Gender",
"TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU",
"TC_final", "LDL_final", "HDL_final", "TG_final",
"hsCRP_plasma",
"systolic", "diastoli", "GFR_MDRD", "BMI",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"restenos", "stenose",
"MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "EP_composite_time",
"macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
"neutrophils", "Mast_cells_plaque",
"IPH.bin", "vessel_density_averaged",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
"IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
"MCP1", "MCP1_pg_ug_2015", "MCP1_pg_ml_2015",
"MCP1_plasma_olink")
basetable_bin = c("Gender",
"KDOQI", "BMI_WHO",
"SmokerStatus", "AlcoholUse",
"DiabetesStatus",
"Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs",
"Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
"Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
"restenos", "stenose",
"CAD_history", "PAOD", "Peripheral.interv",
"EP_composite", "Macrophages.bin", "SMC.bin",
"IPH.bin",
"Calc.bin", "Collagen.bin",
"Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin
basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_conAll patients
Showing the baseline table of the whole Athero-Express Biobank.
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Symptoms.4g",
data = AEDB, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
level Overall Missing
n 3793
Hospital % (freq) St. Antonius, Nieuwegein 45.7 (1735) 0.0
UMC Utrecht 54.3 (2058)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0
2002 2.5 ( 94)
2003 5.4 ( 204)
2004 7.6 ( 289)
2005 8.1 ( 309)
2006 7.5 ( 285)
2007 6.2 ( 234)
2008 5.9 ( 223)
2009 7.1 ( 268)
2010 8.1 ( 307)
2011 7.1 ( 270)
2012 8.2 ( 312)
2013 6.9 ( 262)
2014 7.9 ( 299)
2015 2.1 ( 79)
2016 3.3 ( 124)
2017 2.2 ( 85)
2018 2.1 ( 80)
2019 1.8 ( 69)
Age (mean (SD)) 68.906 (9.322) 0.0
Gender % (freq) female 30.6 (1161) 0.0
male 69.4 (2632)
TC_finalCU (mean (SD)) 185.256 (81.509) 46.8
LDL_finalCU (mean (SD)) 106.533 (40.725) 54.5
HDL_finalCU (mean (SD)) 46.591 (16.725) 51.1
TG_finalCU (mean (SD)) 154.212 (99.774) 51.8
TC_final (mean (SD)) 4.798 (2.111) 46.8
LDL_final (mean (SD)) 2.759 (1.055) 54.5
HDL_final (mean (SD)) 1.207 (0.433) 51.1
TG_final (mean (SD)) 1.743 (1.127) 51.8
hsCRP_plasma (mean (SD)) 19.231 (206.750) 60.6
systolic (mean (SD)) 150.901 (25.114) 13.5
diastoli (mean (SD)) 79.933 (21.847) 13.5
GFR_MDRD (mean (SD)) 74.844 (24.740) 6.5
BMI (mean (SD)) 26.336 (4.050) 7.5
KDOQI % (freq) No data available/missing 0.0 ( 0) 6.6
Normal kidney function 22.1 ( 839)
CKD 2 (Mild) 47.2 (1789)
CKD 3 (Moderate) 21.9 ( 831)
CKD 4 (Severe) 1.4 ( 53)
CKD 5 (Failure) 0.8 ( 32)
<NA> 6.6 ( 249)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 7.5
Underweight 1.2 ( 44)
Normal 35.2 (1336)
Overweight 42.1 (1595)
Obese 14.1 ( 533)
<NA> 7.5 ( 285)
SmokerStatus % (freq) Current smoker 34.5 (1310) 7.4
Ex-smoker 47.8 (1814)
Never smoked 10.3 ( 389)
<NA> 7.4 ( 280)
AlcoholUse % (freq) No 32.6 (1238) 5.5
Yes 61.9 (2346)
<NA> 5.5 ( 209)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 72.9 (2766) 1.1
Diabetes 26.0 ( 985)
<NA> 1.1 ( 42)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 4.0
no 23.7 ( 900)
yes 72.3 (2742)
<NA> 4.0 ( 151)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 5.5
no 28.6 (1086)
yes 65.9 (2500)
<NA> 5.5 ( 207)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 1.3
no 13.3 ( 505)
yes 85.4 (3240)
<NA> 1.3 ( 48)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 1.5
no 21.0 ( 798)
yes 77.5 (2940)
<NA> 1.5 ( 55)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 1.6
no 85.6 (3248)
yes 12.8 ( 485)
<NA> 1.6 ( 60)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 1.6
no 13.7 ( 521)
yes 84.7 (3213)
<NA> 1.6 ( 59)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 1.5
no 21.8 ( 826)
yes 76.7 (2911)
<NA> 1.5 ( 56)
Stroke_Dx % (freq) Missing 0.0 ( 0) 8.1
No stroke diagnosed 74.4 (2823)
Stroke diagnosed 17.5 ( 663)
<NA> 8.1 ( 307)
sympt % (freq) missing 29.1 (1103) 0.0
Asymptomatic 8.8 ( 333)
TIA 27.4 (1040)
minor stroke 12.1 ( 458)
Major stroke 7.3 ( 275)
Amaurosis fugax 10.5 ( 399)
Four vessel disease 1.1 ( 43)
Vertebrobasilary TIA 0.1 ( 5)
Retinal infarction 1.0 ( 37)
Symptomatic, but aspecific symtoms 1.6 ( 61)
Contralateral symptomatic occlusion 0.3 ( 12)
retinal infarction 0.2 ( 6)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 1)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.5 ( 18)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.1 ( 2)
TGA 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 8.8 ( 333) 29.1
Ocular 11.0 ( 417)
Other 3.1 ( 119)
Retinal infarction 1.1 ( 43)
Stroke 19.3 ( 733)
TIA 27.6 (1045)
<NA> 29.1 (1103)
AsymptSympt % (freq) Asymptomatic 8.8 ( 333) 29.1
Ocular and others 15.3 ( 579)
Symptomatic 46.9 (1778)
<NA> 29.1 (1103)
AsymptSympt2G % (freq) Asymptomatic 8.8 ( 333) 29.1
Symptomatic 62.1 (2357)
<NA> 29.1 (1103)
restenos % (freq) missing 0.0 ( 0) 4.0
de novo 87.0 (3299)
restenosis 8.8 ( 334)
stenose bij angioseal na PTCA 0.2 ( 7)
<NA> 4.0 ( 153)
stenose % (freq) missing 0.0 ( 0) 7.0
0-49% 0.7 ( 25)
50-70% 6.8 ( 257)
70-90% 35.6 (1349)
90-99% 29.9 (1133)
100% (Occlusion) 14.8 ( 560)
NA 0.1 ( 3)
50-99% 2.6 ( 99)
70-99% 2.6 ( 100)
99 0.1 ( 2)
<NA> 7.0 ( 265)
MedHx_CVD % (freq) No 34.5 (1310) 0.2
yes 65.3 (2476)
<NA> 0.2 ( 7)
CAD_history % (freq) Missing 0.0 ( 0) 2.0
No history CAD 64.1 (2432)
History CAD 33.9 (1285)
<NA> 2.0 ( 76)
PAOD % (freq) missing/no data 0.0 ( 0) 1.6
no 55.1 (2090)
yes 43.3 (1644)
<NA> 1.6 ( 59)
Peripheral.interv % (freq) no 68.0 (2581) 3.0
yes 29.0 (1099)
<NA> 3.0 ( 113)
EP_composite % (freq) No data available. 0.0 ( 0) 7.3
No composite endpoints 60.6 (2299)
Composite endpoints 32.1 (1218)
<NA> 7.3 ( 276)
EP_composite_time (mean (SD)) 2.267 (1.203) 7.4
macmean0 (mean (SD)) 0.656 (1.154) 32.4
smcmean0 (mean (SD)) 2.292 (6.618) 32.4
Macrophages.bin % (freq) no/minor 42.3 (1603) 25.7
moderate/heavy 32.1 (1216)
<NA> 25.7 ( 974)
SMC.bin % (freq) no/minor 22.9 ( 870) 25.3
moderate/heavy 51.8 (1964)
<NA> 25.3 ( 959)
neutrophils (mean (SD)) 162.985 (490.469) 91.0
Mast_cells_plaque (mean (SD)) 165.663 (163.421) 93.0
IPH.bin % (freq) no 32.3 (1225) 24.8
yes 42.9 (1628)
<NA> 24.8 ( 940)
vessel_density_averaged (mean (SD)) 8.030 (6.344) 48.0
Calc.bin % (freq) no/minor 37.9 (1438) 24.7
moderate/heavy 37.4 (1417)
<NA> 24.7 ( 938)
Collagen.bin % (freq) no/minor 14.2 ( 540) 25.2
moderate/heavy 60.6 (2299)
<NA> 25.2 ( 954)
Fat.bin_10 % (freq) <10% 32.3 (1226) 24.7
>10% 43.0 (1630)
<NA> 24.7 ( 937)
Fat.bin_40 % (freq) <40% 60.0 (2276) 24.7
>40% 15.3 ( 580)
<NA> 24.7 ( 937)
OverallPlaquePhenotype % (freq) atheromatous 14.5 ( 550) 25.3
fibroatheromatous 22.2 ( 843)
fibrous 37.9 (1439)
<NA> 25.3 ( 961)
IL6 (mean (SD)) 94.451 (278.490) 84.5
IL6_pg_ug_2015 (mean (SD)) 0.134 (0.541) 67.2
IL6R_pg_ug_2015 (mean (SD)) 0.211 (0.251) 67.1
MCP1 (mean (SD)) 130.926 (118.422) 83.7
MCP1_pg_ug_2015 (mean (SD)) 0.596 (0.879) 65.4
MCP1_pg_ml_2015 (mean (SD)) 587.541 (843.110) 65.3
MCP1_plasma_olink (mean (SD)) 3.608 (0.578) 81.8 CEA patients
Showing the baseline table of the CEA patients in the Athero-Express Biobank.
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "Symptoms.4g",
data = AEDB.CEA, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
level Overall Missing
n 2423
Hospital % (freq) St. Antonius, Nieuwegein 39.1 ( 948) 0.0
UMC Utrecht 60.9 (1475)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0
2002 3.3 ( 81)
2003 6.5 ( 157)
2004 7.8 ( 190)
2005 7.6 ( 185)
2006 7.6 ( 183)
2007 6.3 ( 152)
2008 5.7 ( 138)
2009 7.5 ( 182)
2010 6.6 ( 159)
2011 6.8 ( 164)
2012 7.3 ( 176)
2013 6.1 ( 149)
2014 6.7 ( 163)
2015 3.1 ( 76)
2016 3.5 ( 85)
2017 2.7 ( 65)
2018 2.7 ( 66)
2019 2.1 ( 52)
Age (mean (SD)) 69.103 (9.302) 0.0
Gender % (freq) female 30.5 ( 739) 0.0
male 69.5 (1684)
TC_finalCU (mean (SD)) 184.852 (56.275) 38.0
LDL_finalCU (mean (SD)) 108.484 (41.794) 45.6
HDL_finalCU (mean (SD)) 46.432 (16.999) 41.7
TG_finalCU (mean (SD)) 151.189 (91.249) 42.8
TC_final (mean (SD)) 4.788 (1.458) 38.0
LDL_final (mean (SD)) 2.810 (1.082) 45.6
HDL_final (mean (SD)) 1.203 (0.440) 41.7
TG_final (mean (SD)) 1.708 (1.031) 42.8
hsCRP_plasma (mean (SD)) 19.887 (231.453) 52.9
systolic (mean (SD)) 152.408 (25.163) 11.3
diastoli (mean (SD)) 81.314 (25.178) 11.3
GFR_MDRD (mean (SD)) 73.115 (21.145) 5.4
BMI (mean (SD)) 26.488 (3.976) 5.9
KDOQI % (freq) No data available/missing 0.0 ( 0) 5.4
Normal kidney function 19.1 ( 462)
CKD 2 (Mild) 50.9 (1233)
CKD 3 (Moderate) 22.9 ( 554)
CKD 4 (Severe) 1.3 ( 32)
CKD 5 (Failure) 0.4 ( 10)
<NA> 5.4 ( 132)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 5.9
Underweight 1.0 ( 24)
Normal 35.1 ( 851)
Overweight 43.4 (1052)
Obese 14.5 ( 352)
<NA> 5.9 ( 144)
SmokerStatus % (freq) Current smoker 33.2 ( 805) 5.9
Ex-smoker 48.0 (1163)
Never smoked 12.9 ( 313)
<NA> 5.9 ( 142)
AlcoholUse % (freq) No 34.5 ( 835) 4.1
Yes 61.5 (1489)
<NA> 4.1 ( 99)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 75.2 (1822) 1.1
Diabetes 23.7 ( 574)
<NA> 1.1 ( 27)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 3.2
no 24.3 ( 590)
yes 72.4 (1755)
<NA> 3.2 ( 78)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 4.4
no 30.0 ( 726)
yes 65.6 (1590)
<NA> 4.4 ( 107)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 1.2
no 14.6 ( 354)
yes 84.2 (2041)
<NA> 1.2 ( 28)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 1.4
no 23.4 ( 566)
yes 75.3 (1824)
<NA> 1.4 ( 33)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 1.6
no 87.3 (2116)
yes 11.1 ( 269)
<NA> 1.6 ( 38)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 1.5
no 12.2 ( 295)
yes 86.3 (2092)
<NA> 1.5 ( 36)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 1.4
no 20.3 ( 491)
yes 78.3 (1898)
<NA> 1.4 ( 34)
Stroke_Dx % (freq) Missing 0.0 ( 0) 6.9
No stroke diagnosed 71.5 (1732)
Stroke diagnosed 21.7 ( 525)
<NA> 6.9 ( 166)
sympt % (freq) missing 0.0 ( 0) 0.0
Asymptomatic 11.1 ( 270)
TIA 39.7 ( 961)
minor stroke 16.8 ( 407)
Major stroke 9.9 ( 239)
Amaurosis fugax 15.7 ( 380)
Four vessel disease 1.6 ( 38)
Vertebrobasilary TIA 0.2 ( 5)
Retinal infarction 1.4 ( 34)
Symptomatic, but aspecific symtoms 2.2 ( 53)
Contralateral symptomatic occlusion 0.5 ( 11)
retinal infarction 0.2 ( 6)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 1)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.7 ( 16)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.1 ( 2)
TGA 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 11.1 ( 270) 0.0
Ocular 16.3 ( 396)
Other 4.3 ( 105)
Retinal infarction 1.7 ( 40)
Stroke 26.7 ( 646)
TIA 39.9 ( 966)
AsymptSympt % (freq) Asymptomatic 11.1 ( 270) 0.0
Ocular and others 22.3 ( 541)
Symptomatic 66.5 (1612)
AsymptSympt2G % (freq) Asymptomatic 11.1 ( 270) 0.0
Symptomatic 88.9 (2153)
restenos % (freq) missing 0.0 ( 0) 1.4
de novo 93.7 (2270)
restenosis 4.9 ( 118)
stenose bij angioseal na PTCA 0.0 ( 0)
<NA> 1.4 ( 35)
stenose % (freq) missing 0.0 ( 0) 2.0
0-49% 0.5 ( 13)
50-70% 7.8 ( 190)
70-90% 46.5 (1127)
90-99% 38.3 ( 928)
100% (Occlusion) 1.3 ( 31)
NA 0.0 ( 1)
50-99% 0.6 ( 15)
70-99% 2.8 ( 68)
99 0.1 ( 2)
<NA> 2.0 ( 48)
MedHx_CVD % (freq) No 36.9 ( 893) 0.0
yes 63.1 (1530)
CAD_history % (freq) Missing 0.0 ( 0) 1.9
No history CAD 66.9 (1620)
History CAD 31.2 ( 756)
<NA> 1.9 ( 47)
PAOD % (freq) missing/no data 0.0 ( 0) 2.0
no 77.5 (1878)
yes 20.5 ( 497)
<NA> 2.0 ( 48)
Peripheral.interv % (freq) no 77.2 (1870) 2.9
yes 19.9 ( 482)
<NA> 2.9 ( 71)
EP_composite % (freq) No data available. 0.0 ( 0) 5.0
No composite endpoints 70.6 (1711)
Composite endpoints 24.3 ( 590)
<NA> 5.0 ( 122)
EP_composite_time (mean (SD)) 2.479 (1.109) 5.2
macmean0 (mean (SD)) 0.767 (1.183) 29.7
smcmean0 (mean (SD)) 1.985 (2.380) 29.9
Macrophages.bin % (freq) no/minor 35.0 ( 847) 24.1
moderate/heavy 40.9 ( 992)
<NA> 24.1 ( 584)
SMC.bin % (freq) no/minor 24.8 ( 602) 23.8
moderate/heavy 51.3 (1244)
<NA> 23.8 ( 577)
neutrophils (mean (SD)) 147.151 (419.998) 87.5
Mast_cells_plaque (mean (SD)) 164.488 (163.771) 90.0
IPH.bin % (freq) no 30.8 ( 746) 23.5
yes 45.7 (1108)
<NA> 23.5 ( 569)
vessel_density_averaged (mean (SD)) 8.317 (6.384) 35.1
Calc.bin % (freq) no/minor 41.6 (1007) 23.4
moderate/heavy 35.1 ( 850)
<NA> 23.4 ( 566)
Collagen.bin % (freq) no/minor 15.8 ( 382) 23.6
moderate/heavy 60.6 (1469)
<NA> 23.6 ( 572)
Fat.bin_10 % (freq) <10% 22.4 ( 542) 23.3
>10% 54.3 (1316)
<NA> 23.3 ( 565)
Fat.bin_40 % (freq) <40% 56.2 (1362) 23.3
>40% 20.5 ( 496)
<NA> 23.3 ( 565)
OverallPlaquePhenotype % (freq) atheromatous 19.8 ( 480) 23.7
fibroatheromatous 27.8 ( 674)
fibrous 28.7 ( 695)
<NA> 23.7 ( 574)
IL6 (mean (SD)) 98.812 (292.457) 78.2
IL6_pg_ug_2015 (mean (SD)) 0.138 (0.556) 52.5
IL6R_pg_ug_2015 (mean (SD)) 0.211 (0.251) 52.4
MCP1 (mean (SD)) 135.763 (120.028) 76.7
MCP1_pg_ug_2015 (mean (SD)) 0.612 (0.904) 50.6
MCP1_pg_ml_2015 (mean (SD)) 600.444 (858.416) 50.5
MCP1_plasma_olink (mean (SD)) 3.608 (0.579) 71.6 CEA patients with MCP1_pg_ug_2015
Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ug_2015.
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015))
AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6]no non-missing arguments to min; returning Infno non-missing arguments to max; returning -InfVariable has only NA's in at least one stratum. na.rm turned off. Stratified by AsymptSympt2G
level Asymptomatic Symptomatic
n 131 1067
Hospital % (freq) St. Antonius, Nieuwegein 50.4 ( 66) 46.5 ( 496)
UMC Utrecht 49.6 ( 65) 53.5 ( 571)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
2002 10.7 ( 14) 3.9 ( 42)
2003 7.6 ( 10) 9.4 ( 100)
2004 17.6 ( 23) 11.5 ( 123)
2005 9.9 ( 13) 11.2 ( 119)
2006 10.7 ( 14) 10.2 ( 109)
2007 11.5 ( 15) 10.5 ( 112)
2008 7.6 ( 10) 7.4 ( 79)
2009 7.6 ( 10) 8.4 ( 90)
2010 5.3 ( 7) 7.6 ( 81)
2011 6.1 ( 8) 9.6 ( 102)
2012 5.3 ( 7) 8.2 ( 88)
2013 0.0 ( 0) 2.0 ( 21)
2014 0.0 ( 0) 0.1 ( 1)
2015 0.0 ( 0) 0.0 ( 0)
2016 0.0 ( 0) 0.0 ( 0)
2017 0.0 ( 0) 0.0 ( 0)
2018 0.0 ( 0) 0.0 ( 0)
2019 0.0 ( 0) 0.0 ( 0)
Age (mean (SD)) 66.237 (9.184) 68.937 (9.119)
Gender % (freq) female 23.7 ( 31) 31.4 ( 335)
male 76.3 (100) 68.6 ( 732)
TC_finalCU (mean (SD)) 175.987 (47.184) 183.526 (48.426)
LDL_finalCU (mean (SD)) 102.781 (38.324) 109.377 (41.109)
HDL_finalCU (mean (SD)) 43.701 (14.754) 45.809 (18.513)
TG_finalCU (mean (SD)) 157.650 (89.246) 145.194 (84.818)
TC_final (mean (SD)) 4.558 (1.222) 4.753 (1.254)
LDL_final (mean (SD)) 2.662 (0.993) 2.833 (1.065)
HDL_final (mean (SD)) 1.132 (0.382) 1.186 (0.479)
TG_final (mean (SD)) 1.781 (1.008) 1.641 (0.958)
hsCRP_plasma (mean (SD)) 5.688 (19.440) 16.551 (113.708)
systolic (mean (SD)) 153.577 (24.327) 155.790 (26.176)
diastoli (mean (SD)) 80.622 (13.225) 82.883 (13.573)
GFR_MDRD (mean (SD)) 71.026 (20.424) 71.866 (20.055)
BMI (mean (SD)) 26.623 (3.391) 26.320 (3.745)
KDOQI % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
Normal kidney function 17.6 ( 23) 17.2 ( 184)
CKD 2 (Mild) 49.6 ( 65) 53.2 ( 568)
CKD 3 (Moderate) 28.2 ( 37) 24.4 ( 260)
CKD 4 (Severe) 0.0 ( 0) 1.2 ( 13)
CKD 5 (Failure) 0.8 ( 1) 0.4 ( 4)
<NA> 3.8 ( 5) 3.6 ( 38)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
Underweight 0.8 ( 1) 0.9 ( 10)
Normal 32.8 ( 43) 35.6 ( 380)
Overweight 51.1 ( 67) 46.1 ( 492)
Obese 13.0 ( 17) 12.7 ( 136)
<NA> 2.3 ( 3) 4.6 ( 49)
SmokerStatus % (freq) Current smoker 30.5 ( 40) 36.3 ( 387)
Ex-smoker 57.3 ( 75) 45.5 ( 486)
Never smoked 9.9 ( 13) 14.2 ( 152)
<NA> 2.3 ( 3) 3.9 ( 42)
AlcoholUse % (freq) No 38.2 ( 50) 33.3 ( 355)
Yes 59.5 ( 78) 62.4 ( 666)
<NA> 2.3 ( 3) 4.3 ( 46)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 76.3 (100) 77.4 ( 826)
Diabetes 23.7 ( 31) 22.6 ( 241)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 23.7 ( 31) 26.6 ( 284)
yes 75.6 ( 99) 71.2 ( 760)
<NA> 0.8 ( 1) 2.2 ( 23)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 30.5 ( 40) 32.9 ( 351)
yes 67.9 ( 89) 64.3 ( 686)
<NA> 1.5 ( 2) 2.8 ( 30)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 9.9 ( 13) 14.3 ( 153)
yes 90.1 (118) 85.7 ( 914)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 14.5 ( 19) 23.3 ( 249)
yes 85.5 (112) 76.5 ( 816)
<NA> 0.0 ( 0) 0.2 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 89.3 (117) 88.0 ( 939)
yes 10.7 ( 14) 11.8 ( 126)
<NA> 0.0 ( 0) 0.2 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 6.1 ( 8) 11.0 ( 117)
yes 93.1 (122) 88.7 ( 946)
<NA> 0.8 ( 1) 0.4 ( 4)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 15.3 ( 20) 22.7 ( 242)
yes 84.7 (111) 77.1 ( 823)
<NA> 0.0 ( 0) 0.2 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0) 0.0 ( 0)
No stroke diagnosed 80.2 (105) 75.2 ( 802)
Stroke diagnosed 14.5 ( 19) 19.5 ( 208)
<NA> 5.3 ( 7) 5.3 ( 57)
sympt % (freq) missing 0.0 ( 0) 0.0 ( 0)
Asymptomatic 100.0 (131) 0.0 ( 0)
TIA 0.0 ( 0) 46.3 ( 494)
minor stroke 0.0 ( 0) 16.7 ( 178)
Major stroke 0.0 ( 0) 12.3 ( 131)
Amaurosis fugax 0.0 ( 0) 17.2 ( 184)
Four vessel disease 0.0 ( 0) 2.2 ( 23)
Vertebrobasilary TIA 0.0 ( 0) 0.2 ( 2)
Retinal infarction 0.0 ( 0) 1.4 ( 15)
Symptomatic, but aspecific symtoms 0.0 ( 0) 2.7 ( 29)
Contralateral symptomatic occlusion 0.0 ( 0) 0.6 ( 6)
retinal infarction 0.0 ( 0) 0.3 ( 3)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0) 0.1 ( 1)
retinal infarction + TIAs 0.0 ( 0) 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0) 0.1 ( 1)
ischemisch glaucoom 0.0 ( 0) 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0) 0.0 ( 0)
TGA 0.0 ( 0) 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 100.0 (131) 0.0 ( 0)
Ocular 0.0 ( 0) 17.3 ( 185)
Other 0.0 ( 0) 5.5 ( 59)
Retinal infarction 0.0 ( 0) 1.7 ( 18)
Stroke 0.0 ( 0) 29.0 ( 309)
TIA 0.0 ( 0) 46.5 ( 496)
AsymptSympt % (freq) Asymptomatic 100.0 (131) 0.0 ( 0)
Ocular and others 0.0 ( 0) 24.6 ( 262)
Symptomatic 0.0 ( 0) 75.4 ( 805)
AsymptSympt2G % (freq) Asymptomatic 100.0 (131) 0.0 ( 0)
Symptomatic 0.0 ( 0) 100.0 (1067)
restenos % (freq) missing 0.0 ( 0) 0.0 ( 0)
de novo 93.9 (123) 94.8 (1011)
restenosis 2.3 ( 3) 3.2 ( 34)
stenose bij angioseal na PTCA 0.0 ( 0) 0.0 ( 0)
<NA> 3.8 ( 5) 2.1 ( 22)
stenose % (freq) missing 0.0 ( 0) 0.0 ( 0)
0-49% 0.0 ( 0) 0.6 ( 6)
50-70% 3.1 ( 4) 6.5 ( 69)
70-90% 51.1 ( 67) 44.5 ( 475)
90-99% 41.2 ( 54) 42.7 ( 456)
100% (Occlusion) 0.0 ( 0) 0.9 ( 10)
NA 0.0 ( 0) 0.0 ( 0)
50-99% 0.8 ( 1) 0.4 ( 4)
70-99% 0.0 ( 0) 1.3 ( 14)
99 0.0 ( 0) 0.0 ( 0)
<NA> 3.8 ( 5) 3.1 ( 33)
MedHx_CVD % (freq) No 38.9 ( 51) 36.9 ( 394)
yes 61.1 ( 80) 63.1 ( 673)
CAD_history % (freq) Missing 0.0 ( 0) 0.0 ( 0)
No history CAD 61.8 ( 81) 69.9 ( 746)
History CAD 38.2 ( 50) 30.1 ( 321)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0 ( 0)
no 74.0 ( 97) 79.6 ( 849)
yes 26.0 ( 34) 20.4 ( 218)
Peripheral.interv % (freq) no 74.0 ( 97) 82.6 ( 881)
yes 26.0 ( 34) 17.2 ( 183)
<NA> 0.0 ( 0) 0.3 ( 3)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0 ( 0)
No composite endpoints 67.2 ( 88) 74.3 ( 793)
Composite endpoints 32.8 ( 43) 24.8 ( 265)
<NA> 0.0 ( 0) 0.8 ( 9)
EP_composite_time (mean (SD)) 2.614 (0.931) 2.614 (1.094)
macmean0 (mean (SD)) 0.837 (1.088) 0.780 (1.230)
smcmean0 (mean (SD)) 2.152 (1.861) 1.905 (2.221)
Macrophages.bin % (freq) no/minor 48.9 ( 64) 47.4 ( 506)
moderate/heavy 50.4 ( 66) 50.5 ( 539)
<NA> 0.8 ( 1) 2.1 ( 22)
SMC.bin % (freq) no/minor 22.9 ( 30) 32.1 ( 343)
moderate/heavy 75.6 ( 99) 66.0 ( 704)
<NA> 1.5 ( 2) 1.9 ( 20)
neutrophils (mean (SD)) 157.643 (507.380) 172.872 (477.038)
Mast_cells_plaque (mean (SD)) 111.400 (112.037) 183.284 (180.156)
IPH.bin % (freq) no 41.2 ( 54) 38.1 ( 406)
yes 58.0 ( 76) 60.2 ( 642)
<NA> 0.8 ( 1) 1.8 ( 19)
vessel_density_averaged (mean (SD)) 8.608 (6.547) 8.406 (6.463)
Stratified by AsymptSympt2G
p test Missing
n
Hospital % (freq) 0.453 0.0
ORyear % (freq) NaN 0.0
Age (mean (SD)) 0.001 0.0
Gender % (freq) 0.087 0.0
TC_finalCU (mean (SD)) 0.174 33.5
LDL_finalCU (mean (SD)) 0.183 39.6
HDL_finalCU (mean (SD)) 0.318 36.4
TG_finalCU (mean (SD)) 0.209 36.1
TC_final (mean (SD)) 0.174 33.5
LDL_final (mean (SD)) 0.183 39.6
HDL_final (mean (SD)) 0.318 36.4
TG_final (mean (SD)) 0.209 36.1
hsCRP_plasma (mean (SD)) 0.380 38.7
systolic (mean (SD)) 0.397 13.9
diastoli (mean (SD)) 0.097 13.9
GFR_MDRD (mean (SD)) 0.658 3.5
BMI (mean (SD)) 0.381 4.2
KDOQI % (freq) NaN 3.6
BMI_WHO % (freq) NaN 4.3
SmokerStatus % (freq) 0.075 3.8
AlcoholUse % (freq) 0.342 4.1
DiabetesStatus % (freq) 0.867 0.0
Hypertension.selfreport % (freq) NaN 2.0
Hypertension.selfreportdrug % (freq) NaN 2.7
Hypertension.composite % (freq) NaN 0.0
Hypertension.drugs % (freq) NaN 0.2
Med.anticoagulants % (freq) NaN 0.2
Med.all.antiplatelet % (freq) NaN 0.4
Med.Statin.LLD % (freq) NaN 0.2
Stroke_Dx % (freq) NaN 5.3
sympt % (freq) NaN 0.0
Symptoms.5G % (freq) <0.001 0.0
AsymptSympt % (freq) <0.001 0.0
AsymptSympt2G % (freq) <0.001 0.0
restenos % (freq) NaN 2.3
stenose % (freq) NaN 3.2
MedHx_CVD % (freq) 0.724 0.0
CAD_history % (freq) NaN 0.0
PAOD % (freq) NaN 0.0
Peripheral.interv % (freq) 0.041 0.3
EP_composite % (freq) NaN 0.8
EP_composite_time (mean (SD)) 0.998 0.9
macmean0 (mean (SD)) 0.618 2.3
smcmean0 (mean (SD)) 0.225 2.7
Macrophages.bin % (freq) 0.584 1.9
SMC.bin % (freq) 0.087 1.8
neutrophils (mean (SD)) 0.876 82.0
Mast_cells_plaque (mean (SD)) 0.056 86.1
IPH.bin % (freq) 0.571 1.7
vessel_density_averaged (mean (SD)) 0.748 8.7
[ reached getOption("max.print") -- omitted 23 rows ]CEA patients with MCP1_pg_ug_2015 and MCP1
Showing the baseline table of the CEA patients in the Athero-Express Biobank with MCP1_pg_ug_2015 and MCP1.
AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) | !is.na(MCP1))
AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
strata = "AsymptSympt2G",
data = AEDB.CEA.subset.combo, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:6]no non-missing arguments to min; returning Infno non-missing arguments to max; returning -InfVariable has only NA's in at least one stratum. na.rm turned off. Stratified by AsymptSympt2G
level Asymptomatic Symptomatic
n 161 1167
Hospital % (freq) St. Antonius, Nieuwegein 52.2 ( 84) 46.9 ( 547)
UMC Utrecht 47.8 ( 77) 53.1 ( 620)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
2002 10.6 ( 17) 4.8 ( 56)
2003 11.8 ( 19) 10.6 ( 124)
2004 19.9 ( 32) 12.2 ( 142)
2005 13.7 ( 22) 13.3 ( 155)
2006 8.7 ( 14) 9.9 ( 116)
2007 9.3 ( 15) 9.6 ( 112)
2008 6.2 ( 10) 6.8 ( 79)
2009 6.2 ( 10) 7.7 ( 90)
2010 4.3 ( 7) 6.9 ( 81)
2011 5.0 ( 8) 8.7 ( 102)
2012 4.3 ( 7) 7.5 ( 88)
2013 0.0 ( 0) 1.8 ( 21)
2014 0.0 ( 0) 0.1 ( 1)
2015 0.0 ( 0) 0.0 ( 0)
2016 0.0 ( 0) 0.0 ( 0)
2017 0.0 ( 0) 0.0 ( 0)
2018 0.0 ( 0) 0.0 ( 0)
2019 0.0 ( 0) 0.0 ( 0)
Age (mean (SD)) 65.901 (9.051) 68.785 (9.081)
Gender % (freq) female 23.0 ( 37) 30.4 ( 355)
male 77.0 (124) 69.6 ( 812)
TC_finalCU (mean (SD)) 179.199 (45.274) 184.078 (48.333)
LDL_finalCU (mean (SD)) 104.132 (37.590) 109.761 (41.318)
HDL_finalCU (mean (SD)) 44.749 (14.890) 45.803 (18.219)
TG_finalCU (mean (SD)) 158.699 (87.584) 145.901 (83.176)
TC_final (mean (SD)) 4.641 (1.173) 4.768 (1.252)
LDL_final (mean (SD)) 2.697 (0.974) 2.843 (1.070)
HDL_final (mean (SD)) 1.159 (0.386) 1.186 (0.472)
TG_final (mean (SD)) 1.793 (0.990) 1.649 (0.940)
hsCRP_plasma (mean (SD)) 6.846 (21.838) 16.179 (110.739)
systolic (mean (SD)) 152.838 (24.600) 155.713 (26.406)
diastoli (mean (SD)) 80.824 (12.855) 82.863 (13.542)
GFR_MDRD (mean (SD)) 70.440 (19.793) 71.890 (20.127)
BMI (mean (SD)) 26.626 (3.572) 26.350 (3.765)
KDOQI % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
Normal kidney function 14.9 ( 24) 17.4 ( 203)
CKD 2 (Mild) 50.9 ( 82) 53.4 ( 623)
CKD 3 (Moderate) 29.8 ( 48) 24.0 ( 280)
CKD 4 (Severe) 0.0 ( 0) 1.3 ( 15)
CKD 5 (Failure) 0.6 ( 1) 0.4 ( 5)
<NA> 3.7 ( 6) 3.5 ( 41)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
Underweight 1.2 ( 2) 0.9 ( 11)
Normal 32.3 ( 52) 35.6 ( 415)
Overweight 49.7 ( 80) 45.6 ( 532)
Obese 13.7 ( 22) 13.1 ( 153)
<NA> 3.1 ( 5) 4.8 ( 56)
SmokerStatus % (freq) Current smoker 29.2 ( 47) 36.1 ( 421)
Ex-smoker 56.5 ( 91) 45.6 ( 532)
Never smoked 11.8 ( 19) 14.1 ( 165)
<NA> 2.5 ( 4) 4.2 ( 49)
AlcoholUse % (freq) No 38.5 ( 62) 33.6 ( 392)
Yes 59.6 ( 96) 62.2 ( 726)
<NA> 1.9 ( 3) 4.2 ( 49)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 78.3 (126) 77.3 ( 902)
Diabetes 21.7 ( 35) 22.7 ( 265)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 25.5 ( 41) 26.6 ( 310)
yes 73.9 (119) 71.4 ( 833)
<NA> 0.6 ( 1) 2.1 ( 24)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 32.3 ( 52) 32.9 ( 384)
yes 66.5 (107) 64.5 ( 753)
<NA> 1.2 ( 2) 2.6 ( 30)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 11.2 ( 18) 14.1 ( 165)
yes 88.8 (143) 85.9 (1002)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 15.5 ( 25) 22.8 ( 266)
yes 83.9 (135) 77.0 ( 899)
<NA> 0.6 ( 1) 0.2 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 89.4 (144) 88.0 (1027)
yes 9.9 ( 16) 11.8 ( 138)
<NA> 0.6 ( 1) 0.2 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 6.2 ( 10) 10.8 ( 126)
yes 92.5 (149) 88.9 (1037)
<NA> 1.2 ( 2) 0.3 ( 4)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.0 ( 0)
no 17.4 ( 28) 23.1 ( 270)
yes 82.0 (132) 76.7 ( 895)
<NA> 0.6 ( 1) 0.2 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0) 0.0 ( 0)
No stroke diagnosed 80.1 (129) 75.5 ( 881)
Stroke diagnosed 13.7 ( 22) 19.1 ( 223)
<NA> 6.2 ( 10) 5.4 ( 63)
sympt % (freq) missing 0.0 ( 0) 0.0 ( 0)
Asymptomatic 100.0 (161) 0.0 ( 0)
TIA 0.0 ( 0) 46.5 ( 543)
minor stroke 0.0 ( 0) 17.1 ( 200)
Major stroke 0.0 ( 0) 11.7 ( 136)
Amaurosis fugax 0.0 ( 0) 17.0 ( 198)
Four vessel disease 0.0 ( 0) 2.1 ( 25)
Vertebrobasilary TIA 0.0 ( 0) 0.2 ( 2)
Retinal infarction 0.0 ( 0) 1.4 ( 16)
Symptomatic, but aspecific symtoms 0.0 ( 0) 3.1 ( 36)
Contralateral symptomatic occlusion 0.0 ( 0) 0.5 ( 6)
retinal infarction 0.0 ( 0) 0.3 ( 3)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0) 0.1 ( 1)
retinal infarction + TIAs 0.0 ( 0) 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0) 0.1 ( 1)
ischemisch glaucoom 0.0 ( 0) 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0) 0.0 ( 0)
TGA 0.0 ( 0) 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 100.0 (161) 0.0 ( 0)
Ocular 0.0 ( 0) 17.1 ( 199)
Other 0.0 ( 0) 5.8 ( 68)
Retinal infarction 0.0 ( 0) 1.6 ( 19)
Stroke 0.0 ( 0) 28.8 ( 336)
TIA 0.0 ( 0) 46.7 ( 545)
AsymptSympt % (freq) Asymptomatic 100.0 (161) 0.0 ( 0)
Ocular and others 0.0 ( 0) 24.5 ( 286)
Symptomatic 0.0 ( 0) 75.5 ( 881)
AsymptSympt2G % (freq) Asymptomatic 100.0 (161) 0.0 ( 0)
Symptomatic 0.0 ( 0) 100.0 (1167)
restenos % (freq) missing 0.0 ( 0) 0.0 ( 0)
de novo 93.2 (150) 95.0 (1109)
restenosis 3.7 ( 6) 3.1 ( 36)
stenose bij angioseal na PTCA 0.0 ( 0) 0.0 ( 0)
<NA> 3.1 ( 5) 1.9 ( 22)
stenose % (freq) missing 0.0 ( 0) 0.0 ( 0)
0-49% 0.0 ( 0) 0.6 ( 7)
50-70% 2.5 ( 4) 6.3 ( 73)
70-90% 50.9 ( 82) 44.6 ( 520)
90-99% 42.9 ( 69) 43.3 ( 505)
100% (Occlusion) 0.0 ( 0) 0.9 ( 11)
NA 0.0 ( 0) 0.0 ( 0)
50-99% 0.6 ( 1) 0.3 ( 4)
70-99% 0.0 ( 0) 1.2 ( 14)
99 0.0 ( 0) 0.0 ( 0)
<NA> 3.1 ( 5) 2.8 ( 33)
MedHx_CVD % (freq) No 37.3 ( 60) 36.8 ( 429)
yes 62.7 (101) 63.2 ( 738)
CAD_history % (freq) Missing 0.0 ( 0) 0.0 ( 0)
No history CAD 59.0 ( 95) 69.2 ( 807)
History CAD 41.0 ( 66) 30.8 ( 360)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0 ( 0)
no 73.9 (119) 79.9 ( 932)
yes 26.1 ( 42) 20.1 ( 235)
Peripheral.interv % (freq) no 72.7 (117) 83.0 ( 969)
yes 27.3 ( 44) 16.7 ( 195)
<NA> 0.0 ( 0) 0.3 ( 3)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0 ( 0)
No composite endpoints 68.3 (110) 73.9 ( 862)
Composite endpoints 31.7 ( 51) 25.2 ( 294)
<NA> 0.0 ( 0) 0.9 ( 11)
EP_composite_time (mean (SD)) 2.579 (0.961) 2.612 (1.129)
macmean0 (mean (SD)) 0.802 (1.072) 0.821 (1.275)
smcmean0 (mean (SD)) 2.445 (2.594) 1.924 (2.233)
Macrophages.bin % (freq) no/minor 50.3 ( 81) 45.8 ( 534)
moderate/heavy 49.1 ( 79) 52.3 ( 610)
<NA> 0.6 ( 1) 2.0 ( 23)
SMC.bin % (freq) no/minor 21.7 ( 35) 32.5 ( 379)
moderate/heavy 77.0 (124) 65.8 ( 768)
<NA> 1.2 ( 2) 1.7 ( 20)
neutrophils (mean (SD)) 133.447 (437.032) 158.140 (448.512)
Mast_cells_plaque (mean (SD)) 123.389 (135.924) 173.244 (168.601)
IPH.bin % (freq) no 39.1 ( 63) 36.4 ( 425)
yes 60.2 ( 97) 62.0 ( 723)
<NA> 0.6 ( 1) 1.6 ( 19)
vessel_density_averaged (mean (SD)) 8.837 (6.727) 8.438 (6.388)
Stratified by AsymptSympt2G
p test Missing
n
Hospital % (freq) 0.239 0.0
ORyear % (freq) NaN 0.0
Age (mean (SD)) <0.001 0.0
Gender % (freq) 0.065 0.0
TC_finalCU (mean (SD)) 0.322 32.8
LDL_finalCU (mean (SD)) 0.206 39.8
HDL_finalCU (mean (SD)) 0.570 36.1
TG_finalCU (mean (SD)) 0.141 35.6
TC_final (mean (SD)) 0.322 32.8
LDL_final (mean (SD)) 0.206 39.8
HDL_final (mean (SD)) 0.570 36.1
TG_final (mean (SD)) 0.141 35.6
hsCRP_plasma (mean (SD)) 0.394 40.6
systolic (mean (SD)) 0.230 13.5
diastoli (mean (SD)) 0.097 13.5
GFR_MDRD (mean (SD)) 0.400 3.5
BMI (mean (SD)) 0.387 4.4
KDOQI % (freq) NaN 3.5
BMI_WHO % (freq) NaN 4.6
SmokerStatus % (freq) 0.069 4.0
AlcoholUse % (freq) 0.210 3.9
DiabetesStatus % (freq) 0.861 0.0
Hypertension.selfreport % (freq) NaN 1.9
Hypertension.selfreportdrug % (freq) NaN 2.4
Hypertension.composite % (freq) NaN 0.0
Hypertension.drugs % (freq) NaN 0.2
Med.anticoagulants % (freq) NaN 0.2
Med.all.antiplatelet % (freq) NaN 0.5
Med.Statin.LLD % (freq) NaN 0.2
Stroke_Dx % (freq) NaN 5.5
sympt % (freq) NaN 0.0
Symptoms.5G % (freq) <0.001 0.0
AsymptSympt % (freq) <0.001 0.0
AsymptSympt2G % (freq) <0.001 0.0
restenos % (freq) NaN 2.0
stenose % (freq) NaN 2.9
MedHx_CVD % (freq) 0.970 0.0
CAD_history % (freq) NaN 0.0
PAOD % (freq) NaN 0.0
Peripheral.interv % (freq) 0.004 0.2
EP_composite % (freq) NaN 0.8
EP_composite_time (mean (SD)) 0.729 1.0
macmean0 (mean (SD)) 0.862 2.2
smcmean0 (mean (SD)) 0.007 2.5
Macrophages.bin % (freq) 0.311 1.8
SMC.bin % (freq) 0.018 1.7
neutrophils (mean (SD)) 0.754 80.9
Mast_cells_plaque (mean (SD)) 0.097 83.7
IPH.bin % (freq) 0.521 1.5
vessel_density_averaged (mean (SD)) 0.478 8.0
[ reached getOption("max.print") -- omitted 23 rows ]CEA patients with plasma MCP1 levels
Showing the baseline table of the CEA patients in the Athero-Express Biobank with plasma MCP1 levels.
Note plasma MCP1 was only measured in symptomatic patients.
AEDB.CEA.subset.plasma <- subset(AEDB.CEA, !is.na(MCP1_plasma_olink))
AEDB.CEA.subset.plasma.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "AsymptSympt2G",
data = AEDB.CEA.subset.plasma, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
level Overall Missing
n 687
Hospital % (freq) St. Antonius, Nieuwegein 43.2 (297) 0.0
UMC Utrecht 56.8 (390)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0
2002 3.6 ( 25)
2003 7.3 ( 50)
2004 5.2 ( 36)
2005 6.4 ( 44)
2006 6.0 ( 41)
2007 7.4 ( 51)
2008 7.0 ( 48)
2009 9.9 ( 68)
2010 7.6 ( 52)
2011 7.6 ( 52)
2012 7.3 ( 50)
2013 8.6 ( 59)
2014 7.0 ( 48)
2015 1.3 ( 9)
2016 4.7 ( 32)
2017 2.9 ( 20)
2018 0.3 ( 2)
2019 0.0 ( 0)
Age (mean (SD)) 69.898 (9.153) 0.0
Gender % (freq) female 31.6 (217) 0.0
male 68.4 (470)
TC_finalCU (mean (SD)) 179.475 (45.383) 30.1
LDL_finalCU (mean (SD)) 105.024 (39.821) 33.3
HDL_finalCU (mean (SD)) 46.057 (15.806) 32.8
TG_finalCU (mean (SD)) 139.370 (78.941) 33.8
TC_final (mean (SD)) 4.648 (1.175) 30.1
LDL_final (mean (SD)) 2.720 (1.031) 33.3
HDL_final (mean (SD)) 1.193 (0.409) 32.8
TG_final (mean (SD)) 1.575 (0.892) 33.8
hsCRP_plasma (mean (SD)) 26.267 (315.609) 19.7
systolic (mean (SD)) 151.955 (24.817) 10.2
diastoli (mean (SD)) 81.633 (30.976) 10.3
GFR_MDRD (mean (SD)) 72.860 (20.885) 4.9
BMI (mean (SD)) 26.209 (3.847) 4.4
KDOQI % (freq) No data available/missing 0.0 ( 0) 4.9
Normal kidney function 17.9 (123)
CKD 2 (Mild) 52.1 (358)
CKD 3 (Moderate) 23.3 (160)
CKD 4 (Severe) 1.6 ( 11)
CKD 5 (Failure) 0.1 ( 1)
<NA> 4.9 ( 34)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 4.5
Underweight 0.9 ( 6)
Normal 39.7 (273)
Overweight 42.1 (289)
Obese 12.8 ( 88)
<NA> 4.5 ( 31)
SmokerStatus % (freq) Current smoker 35.1 (241) 4.2
Ex-smoker 48.3 (332)
Never smoked 12.4 ( 85)
<NA> 4.2 ( 29)
AlcoholUse % (freq) No 34.5 (237) 3.5
Yes 62.0 (426)
<NA> 3.5 ( 24)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 76.3 (524) 0.0
Diabetes 23.7 (163)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 2.9
no 28.4 (195)
yes 68.7 (472)
<NA> 2.9 ( 20)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 3.1
no 34.5 (237)
yes 62.4 (429)
<NA> 3.1 ( 21)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.1
no 19.1 (131)
yes 80.8 (555)
<NA> 0.1 ( 1)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.3
no 27.2 (187)
yes 72.5 (498)
<NA> 0.3 ( 2)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.3
no 88.8 (610)
yes 10.9 ( 75)
<NA> 0.3 ( 2)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.4
no 13.1 ( 90)
yes 86.5 (594)
<NA> 0.4 ( 3)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.3
no 22.3 (153)
yes 77.4 (532)
<NA> 0.3 ( 2)
Stroke_Dx % (freq) Missing 0.0 ( 0) 4.7
No stroke diagnosed 70.6 (485)
Stroke diagnosed 24.7 (170)
<NA> 4.7 ( 32)
sympt % (freq) missing 0.0 ( 0) 0.0
Asymptomatic 0.0 ( 0)
TIA 46.9 (322)
minor stroke 20.5 (141)
Major stroke 11.1 ( 76)
Amaurosis fugax 16.4 (113)
Four vessel disease 0.0 ( 0)
Vertebrobasilary TIA 0.1 ( 1)
Retinal infarction 2.3 ( 16)
Symptomatic, but aspecific symtoms 2.5 ( 17)
Contralateral symptomatic occlusion 0.0 ( 0)
retinal infarction 0.1 ( 1)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0)
TGA 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 0.0 ( 0) 0.0
Ocular 16.4 (113)
Other 2.5 ( 17)
Retinal infarction 2.5 ( 17)
Stroke 31.6 (217)
TIA 47.0 (323)
AsymptSympt % (freq) Asymptomatic 0.0 ( 0) 0.0
Ocular and others 21.4 (147)
Symptomatic 78.6 (540)
AsymptSympt2G % (freq) Asymptomatic 0.0 ( 0) 0.0
Symptomatic 100.0 (687)
restenos % (freq) missing 0.0 ( 0) 1.6
de novo 94.5 (649)
restenosis 3.9 ( 27)
stenose bij angioseal na PTCA 0.0 ( 0)
<NA> 1.6 ( 11)
stenose % (freq) missing 0.0 ( 0) 2.8
0-49% 0.9 ( 6)
50-70% 8.4 ( 58)
70-90% 45.6 (313)
90-99% 38.3 (263)
100% (Occlusion) 0.7 ( 5)
NA 0.0 ( 0)
50-99% 0.9 ( 6)
70-99% 2.3 ( 16)
99 0.1 ( 1)
<NA> 2.8 ( 19)
MedHx_CVD % (freq) No 38.0 (261) 0.0
yes 62.0 (426)
CAD_history % (freq) Missing 0.0 ( 0) 0.4
No history CAD 70.5 (484)
History CAD 29.1 (200)
<NA> 0.4 ( 3)
PAOD % (freq) missing/no data 0.0 ( 0) 0.7
no 82.4 (566)
yes 16.9 (116)
<NA> 0.7 ( 5)
Peripheral.interv % (freq) no 81.5 (560) 1.2
yes 17.3 (119)
<NA> 1.2 ( 8)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0
No composite endpoints 76.3 (524)
Composite endpoints 23.7 (163)
EP_composite_time (mean (SD)) 2.477 (1.073) 0.0
macmean0 (mean (SD)) 0.782 (1.299) 27.1
smcmean0 (mean (SD)) 1.814 (1.971) 27.4
Macrophages.bin % (freq) no/minor 37.0 (254) 20.7
moderate/heavy 42.4 (291)
<NA> 20.7 (142)
SMC.bin % (freq) no/minor 26.1 (179) 20.7
moderate/heavy 53.3 (366)
<NA> 20.7 (142)
neutrophils (mean (SD)) 174.356 (263.588) 91.4
Mast_cells_plaque (mean (SD)) 157.617 (143.920) 91.3
IPH.bin % (freq) no 30.7 (211) 20.1
yes 49.2 (338)
<NA> 20.1 (138)
vessel_density_averaged (mean (SD)) 7.893 (6.016) 33.8
Calc.bin % (freq) no/minor 44.5 (306) 19.9
moderate/heavy 35.5 (244)
<NA> 19.9 (137)
Collagen.bin % (freq) no/minor 19.8 (136) 20.2
moderate/heavy 60.0 (412)
<NA> 20.2 (139)
Fat.bin_10 % (freq) <10% 22.4 (154) 19.9
>10% 57.6 (396)
<NA> 19.9 (137)
Fat.bin_40 % (freq) <40% 58.1 (399) 19.9
>40% 22.0 (151)
<NA> 19.9 (137)
OverallPlaquePhenotype % (freq) atheromatous 21.4 (147) 20.1
fibroatheromatous 29.1 (200)
fibrous 29.4 (202)
<NA> 20.1 (138)
IL6 (mean (SD)) 71.003 (104.852) 79.9
IL6_pg_ug_2015 (mean (SD)) 0.125 (0.326) 47.7
IL6R_pg_ug_2015 (mean (SD)) 0.228 (0.249) 47.3
MCP1 (mean (SD)) 142.240 (111.921) 78.7
MCP1_pg_ug_2015 (mean (SD)) 0.657 (0.787) 45.6
MCP1_pg_ml_2015 (mean (SD)) 636.467 (705.888) 45.6
MCP1_plasma_olink (mean (SD)) 3.608 (0.579) 0.0 CEA patients with plasma and plaque MCP1 levels
Showing the baseline table of the CEA patients in the Athero-Express Biobank with both plasma and plaque MCP1 levels.
Note plasma MCP1 levels were only measured in symptomatic patients
AEDB.CEA.subset.both <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) & !is.na(MCP1_plasma_olink))
AEDB.CEA.subset.both.tableOne = print(CreateTableOne(vars = basetable_vars,
# factorVars = basetable_bin,
# strata = "AsymptSympt2G",
data = AEDB.CEA.subset.both, includeNA = TRUE),
nonnormal = c(), missing = TRUE,
quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
format = "pf",
contDigits = 3)[,1:3]
level Overall Missing
n 374
Hospital % (freq) St. Antonius, Nieuwegein 51.9 (194) 0.0
UMC Utrecht 48.1 (180)
ORyear % (freq) No data available/missing 0.0 ( 0) 0.0
2002 5.1 ( 19)
2003 10.4 ( 39)
2004 8.8 ( 33)
2005 10.2 ( 38)
2006 7.0 ( 26)
2007 11.5 ( 43)
2008 7.8 ( 29)
2009 11.2 ( 42)
2010 7.5 ( 28)
2011 10.4 ( 39)
2012 8.6 ( 32)
2013 1.6 ( 6)
2014 0.0 ( 0)
2015 0.0 ( 0)
2016 0.0 ( 0)
2017 0.0 ( 0)
2018 0.0 ( 0)
2019 0.0 ( 0)
Age (mean (SD)) 69.762 (9.020) 0.0
Gender % (freq) female 31.3 (117) 0.0
male 68.7 (257)
TC_finalCU (mean (SD)) 178.919 (45.544) 21.4
LDL_finalCU (mean (SD)) 106.741 (39.368) 22.2
HDL_finalCU (mean (SD)) 44.661 (14.630) 22.2
TG_finalCU (mean (SD)) 137.091 (76.469) 22.2
TC_final (mean (SD)) 4.634 (1.180) 21.4
LDL_final (mean (SD)) 2.765 (1.020) 22.2
HDL_final (mean (SD)) 1.157 (0.379) 22.2
TG_final (mean (SD)) 1.549 (0.864) 22.2
hsCRP_plasma (mean (SD)) 14.658 (88.046) 3.7
systolic (mean (SD)) 154.442 (26.409) 12.8
diastoli (mean (SD)) 81.304 (13.680) 12.8
GFR_MDRD (mean (SD)) 71.730 (19.781) 2.4
BMI (mean (SD)) 25.986 (3.619) 5.1
KDOQI % (freq) No data available/missing 0.0 ( 0) 2.4
Normal kidney function 15.8 ( 59)
CKD 2 (Mild) 54.8 (205)
CKD 3 (Moderate) 25.4 ( 95)
CKD 4 (Severe) 1.3 ( 5)
CKD 5 (Failure) 0.3 ( 1)
<NA> 2.4 ( 9)
BMI_WHO % (freq) No data available/missing 0.0 ( 0) 5.3
Underweight 0.8 ( 3)
Normal 39.6 (148)
Overweight 44.7 (167)
Obese 9.6 ( 36)
<NA> 5.3 ( 20)
SmokerStatus % (freq) Current smoker 37.2 (139) 3.2
Ex-smoker 48.9 (183)
Never smoked 10.7 ( 40)
<NA> 3.2 ( 12)
AlcoholUse % (freq) No 32.4 (121) 5.1
Yes 62.6 (234)
<NA> 5.1 ( 19)
DiabetesStatus % (freq) Control (no Diabetes Dx/Med) 79.4 (297) 0.0
Diabetes 20.6 ( 77)
Hypertension.selfreport % (freq) No data available/missing 0.0 ( 0) 2.1
no 30.2 (113)
yes 67.6 (253)
<NA> 2.1 ( 8)
Hypertension.selfreportdrug % (freq) No data available/missing 0.0 ( 0) 2.7
no 37.2 (139)
yes 60.2 (225)
<NA> 2.7 ( 10)
Hypertension.composite % (freq) No data available/missing 0.0 ( 0) 0.0
no 17.6 ( 66)
yes 82.4 (308)
Hypertension.drugs % (freq) No data available/missing 0.0 ( 0) 0.3
no 25.4 ( 95)
yes 74.3 (278)
<NA> 0.3 ( 1)
Med.anticoagulants % (freq) No data available/missing 0.0 ( 0) 0.3
no 87.2 (326)
yes 12.6 ( 47)
<NA> 0.3 ( 1)
Med.all.antiplatelet % (freq) No data available/missing 0.0 ( 0) 0.5
no 11.5 ( 43)
yes 88.0 (329)
<NA> 0.5 ( 2)
Med.Statin.LLD % (freq) No data available/missing 0.0 ( 0) 0.3
no 25.1 ( 94)
yes 74.6 (279)
<NA> 0.3 ( 1)
Stroke_Dx % (freq) Missing 0.0 ( 0) 3.7
No stroke diagnosed 76.7 (287)
Stroke diagnosed 19.5 ( 73)
<NA> 3.7 ( 14)
sympt % (freq) missing 0.0 ( 0) 0.0
Asymptomatic 0.0 ( 0)
TIA 48.9 (183)
minor stroke 16.6 ( 62)
Major stroke 11.8 ( 44)
Amaurosis fugax 17.6 ( 66)
Four vessel disease 0.0 ( 0)
Vertebrobasilary TIA 0.0 ( 0)
Retinal infarction 1.9 ( 7)
Symptomatic, but aspecific symtoms 2.9 ( 11)
Contralateral symptomatic occlusion 0.0 ( 0)
retinal infarction 0.3 ( 1)
armclaudication due to occlusion subclavian artery, CEA needed for bypass 0.0 ( 0)
retinal infarction + TIAs 0.0 ( 0)
Ocular ischemic syndrome 0.0 ( 0)
ischemisch glaucoom 0.0 ( 0)
subclavian steal syndrome 0.0 ( 0)
TGA 0.0 ( 0)
Symptoms.5G % (freq) Asymptomatic 0.0 ( 0) 0.0
Ocular 17.6 ( 66)
Other 2.9 ( 11)
Retinal infarction 2.1 ( 8)
Stroke 28.3 (106)
TIA 48.9 (183)
AsymptSympt % (freq) Asymptomatic 0.0 ( 0) 0.0
Ocular and others 22.7 ( 85)
Symptomatic 77.3 (289)
AsymptSympt2G % (freq) Asymptomatic 0.0 ( 0) 0.0
Symptomatic 100.0 (374)
restenos % (freq) missing 0.0 ( 0) 2.4
de novo 93.9 (351)
restenosis 3.7 ( 14)
stenose bij angioseal na PTCA 0.0 ( 0)
<NA> 2.4 ( 9)
stenose % (freq) missing 0.0 ( 0) 4.0
0-49% 0.5 ( 2)
50-70% 6.4 ( 24)
70-90% 44.1 (165)
90-99% 42.8 (160)
100% (Occlusion) 0.5 ( 2)
NA 0.0 ( 0)
50-99% 0.5 ( 2)
70-99% 1.1 ( 4)
99 0.0 ( 0)
<NA> 4.0 ( 15)
MedHx_CVD % (freq) No 38.8 (145) 0.0
yes 61.2 (229)
CAD_history % (freq) Missing 0.0 ( 0) 0.0
No history CAD 69.3 (259)
History CAD 30.7 (115)
PAOD % (freq) missing/no data 0.0 ( 0) 0.0
no 81.8 (306)
yes 18.2 ( 68)
Peripheral.interv % (freq) no 82.4 (308) 0.0
yes 17.6 ( 66)
EP_composite % (freq) No data available. 0.0 ( 0) 0.0
No composite endpoints 74.3 (278)
Composite endpoints 25.7 ( 96)
EP_composite_time (mean (SD)) 2.533 (1.093) 0.0
macmean0 (mean (SD)) 0.813 (1.372) 2.1
smcmean0 (mean (SD)) 1.843 (1.998) 2.7
Macrophages.bin % (freq) no/minor 48.1 (180) 2.4
moderate/heavy 49.5 (185)
<NA> 2.4 ( 9)
SMC.bin % (freq) no/minor 30.2 (113) 1.9
moderate/heavy 67.9 (254)
<NA> 1.9 ( 7)
neutrophils (mean (SD)) 166.213 (205.019) 87.4
Mast_cells_plaque (mean (SD)) 164.326 (151.274) 87.7
IPH.bin % (freq) no 38.8 (145) 1.9
yes 59.4 (222)
<NA> 1.9 ( 7)
vessel_density_averaged (mean (SD)) 8.112 (6.097) 9.4
Calc.bin % (freq) no/minor 51.9 (194) 1.6
moderate/heavy 46.5 (174)
<NA> 1.6 ( 6)
Collagen.bin % (freq) no/minor 24.6 ( 92) 1.6
moderate/heavy 73.8 (276)
<NA> 1.6 ( 6)
Fat.bin_10 % (freq) <10% 28.6 (107) 1.6
>10% 69.8 (261)
<NA> 1.6 ( 6)
Fat.bin_40 % (freq) <40% 73.0 (273) 1.6
>40% 25.4 ( 95)
<NA> 1.6 ( 6)
OverallPlaquePhenotype % (freq) atheromatous 25.4 ( 95) 1.6
fibroatheromatous 35.8 (134)
fibrous 37.2 (139)
<NA> 1.6 ( 6)
IL6 (mean (SD)) 75.549 (110.399) 67.6
IL6_pg_ug_2015 (mean (SD)) 0.125 (0.326) 4.0
IL6R_pg_ug_2015 (mean (SD)) 0.228 (0.249) 3.2
MCP1 (mean (SD)) 142.029 (110.233) 66.0
MCP1_pg_ug_2015 (mean (SD)) 0.657 (0.787) 0.0
MCP1_pg_ml_2015 (mean (SD)) 636.467 (705.888) 0.0
MCP1_plasma_olink (mean (SD)) 3.602 (0.488) 0.0 Writing the baseline table to Excel format.
# Write basetable
require(openxlsx)
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
AEDB.CEA.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "wholeAEDB_Baseline")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
AEDB.CEA.subset.AsymptSympt.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "wholeAEDB_Baseline_Sympt")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
AEDB.CEA.subset.combo.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "subsetAEDB_Baseline")
write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAplasma.xlsx"),
AEDB.CEA.subset.plasma.tableOne,
row.names = TRUE,
col.names = TRUE,
sheetName = "subsetAEDB_Baseline_plasma")
Data exploration
Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation.
MCP1 plaque levels: experiment 2
We will explore the plaque levels. As noted above, we will use MCP1_pg_ug_2015, this was experiment 2 in 2015 on the LUMINEX-platform and measurements were corrected for total plaque protein content.
summary(AEDB.CEA$MCP1_pg_ug_2015) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0005 0.1374 0.3407 0.6123 0.7235 10.8540 1225 do.call(rbind , by(AEDB.CEA$MCP1_pg_ug_2015, AEDB.CEA$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic 0.0061401090 0.09779678 0.2148984 0.4950529 0.4982360 5.761795 139
Symptomatic 0.0004584575 0.14499491 0.3510850 0.6266503 0.7427464 10.853968 1086summary(AEDB.CEA$MCP1_pg_ml_2015) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.66 101.34 298.76 600.44 770.98 10181.08 1224 do.call(rbind , by(AEDB.CEA$MCP1_pg_ml_2015, AEDB.CEA$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic 9.36 71.1650 152.220 405.1822 537.9100 2669.59 139
Symptomatic 0.66 114.9425 314.625 624.3948 792.4225 10181.08 1085library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015[1] 0.0004584575AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
# title = "MCP1 plaque levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p2, p3)We will explore the MCP1_pg_ml_2015 levels and compare to the protein content corrected ones.
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.min_MCP1_pg_ml_2015 <- min(AEDB.CEA$MCP1_pg_ml_2015, na.rm = TRUE)
min_MCP1_pg_ml_2015[1] 0.66AEDB.CEA$MCP1_pg_ml_2015_LN <- log(AEDB.CEA$MCP1_pg_ml_2015 + min_MCP1_pg_ml_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
# title = "MCP1 plaque levels",
xlab = "natural log-transformed pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.AEDB.CEA$MCP1_pg_ml_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ml_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ml_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "inverse-normal transformation pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p2, p3)MCP1 plaque levels: experiment 1
We will explore the plaque levels. As noted above, we will use MCP1, this was experiment 1 on the LUMINEX-platform and measurements were corrected for total plaque protein content.
# summary(AEDB.CEA$MCP1)
#
# do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
#
attach(AEDB.CEA)
AEDB.CEA$MCP1[MCP1 == 0] <- NA
detach(AEDB.CEA)
summary(AEDB.CEA$MCP1) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
3.865 58.057 103.811 137.960 180.297 926.273 1867 do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic 15.578813 45.31926 77.84731 119.4878 126.1851 846.5306 184
Symptomatic 3.864774 60.54905 111.87004 141.3406 186.4375 926.2729 1683p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "pg/mL",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1[1] 3.864774AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "natural log-transformed pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plaque levels",
xlab = "inverse-normal transformation pg/ug",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p2, p3)
Correlations between MCP1 plaque levels and transformations
Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).
p1 <- ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_rank",
y = "MCP1_pg_ml_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "experiment 1",
ylab = "experiment 2",
title = "MCP1 plaque levels, INT, [pg/mL]",
ggtheme = theme_minimal())
p1 
p2 <- ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_rank",
y = "MCP1_pg_ug_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "experiment 1",
ylab = "experiment 2",
title = "MCP1 plaque levels, INT, [pg/mL]/[pg/ug]",
ggtheme = theme_minimal())
p2 
p3 <- ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_pg_ml_2015_rank",
y = "MCP1_pg_ug_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "experiment 2, [pg/mL]",
ylab = "experiment 2, [pg/ug]",
title = "MCP1 plaque levels, INT",
ggtheme = theme_minimal())
p3
MCP1 plasma levels
Note plasma MCP1 levels were only measured in symptomatic patients.
We will explore the plasma levels. As noted above, we will use MCP1, this was measured in plasma on the OLINK-platform and measurements are given in arbitrary units.
# summary(AEDB.CEA$MCP1)
#
# do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
#
# attach(AEDB.CEA)
# AEDB.CEA$MCP1[MCP1 == 0] <- NA
# detach(AEDB.CEA)
summary(AEDB.CEA$MCP1_plasma_olink) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.6649 3.3219 3.5617 3.6081 3.8117 12.2641 1736 do.call(rbind , by(AEDB.CEA$MCP1_plasma_olink, AEDB.CEA$AsymptSympt2G, summary)) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
Asymptomatic NA NA NA NaN NA NA 270
Symptomatic 0.66491 3.321885 3.56166 3.608137 3.81167 12.26413 1466p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_plasma_olink",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plasma levels",
xlab = "AU",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.min_MCP1_plasma_olink <- min(AEDB.CEA$MCP1_plasma_olink, na.rm = TRUE)
min_MCP1_plasma_olink[1] 0.66491AEDB.CEA$MCP1_plasma_olink_LN <- log(AEDB.CEA$MCP1_plasma_olink + min_MCP1_plasma_olink)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_plasma_olink_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
# add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plasma levels",
xlab = "natural log-transformed AU",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.AEDB.CEA$MCP1_plasma_olink_rank <- qnorm((rank(AEDB.CEA$MCP1_plasma_olink, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_plasma_olink)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_plasma_olink_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = "MCP1 plasma levels",
xlab = "inverse-normal transformation AU",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "") | ggpar(p3, legend = "right")
rm(p1, p2, p3)
Based on the inverse-rank normal transformation we conclude there are no outliers and the data approximates a normal distribution. #### Correlations between MCP1 plaque and plasma levels
Here we compare the MCP1 plaque levels from experiment 1 and 2 (plaque) with plasma levels measured on the OLINK-platform. Given the arbitrary units used in the OLINK-method, we compare only inverse-normal transformed data.
p1 <- ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_rank",
y = "MCP1_pg_ml_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "plaque (experiment 1)",
ylab = "plaque (experiment 2)",
title = "MCP1 plaque levels, INT, [pg/mL]",
ggtheme = theme_minimal())
p1 
p2 <- ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_rank",
y = "MCP1_plasma_olink_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "plaque (experiment 1)",
ylab = "plasma (OLINK)",
title = "MCP1 levels, INT, [pg/mL]/[AU]",
ggtheme = theme_minimal())
p2 
p3 <- ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_pg_ml_2015_rank",
y = "MCP1_plasma_olink_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "plaque (experiment 2)",
ylab = "plasma (OLINK)",
title = "MCP1 levels, INT, [pg/mL]/[AU]",
ggtheme = theme_minimal())
p3
Preliminary conclusion data exploration
In line with the previous work by Marios Georgakis we will apply natural log transformation on all proteins and focus the analysis on MCP1 in plasma and plaque.
Analyses
The analyses are focused on three elements:
- plaque vulnerability phenotypes
- clinical status at inclusion (symptoms)
- secondary clinical outcome during three (3) years of follow-up
Covariates & other variables
- Age (continuous in 1-year increment). [
Age] - Sex (male vs. female). [
Gender] - Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [
Hypertension.composite] - Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [
DiabetesStatus] - Smoking (current, ex-, never). [
SmokerStatus] - LDL-C levels (continuous). [
LDL_final] - Use of lipid-lowering drugs. [
Med.Statin.LLD] - Use of antiplatelet drugs. [
Med.all.antiplatelet] - eGFR (continuous). [
GFR_MDRD] - BMI (continuous). [
BMI] - History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [
MedHx_CVD] combination of [CAD_history,Stroke_history,Peripheral.interv] - Level of stenosis (50-70% vs. 70-99%). [
stenose] - Year of surgery [
ORdate_year] as we discovered in Van Lammeren et al. the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.
Models
We will analyze the data through four different models
- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)
A. Cross-sectional analysis plaque phenotypes
In the cross-sectional analysis of plaque and plasma MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:
- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)
Continous traits
# macrophages
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$macmean0) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0733 0.3133 0.7671 0.9967 15.1000 720 min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))
Minimum value % macrophages: 0.AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)
ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% macrophages",
xlab = "natural log-transformed %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% macrophages",
xlab = "inverse-rank normalized %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
# smooth muscle cells
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$macmean0) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.0733 0.3133 0.7671 0.9967 15.1000 720 min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))
Minimum value % smooth muscle cells: 0.AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)
ggpubr::gghistogram(AEDB.CEA, "SMC_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% smooth muscle cells",
xlab = "natural log-transformed %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "% smooth muscle cells",
xlab = "inverse-rank normalized %",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
# vessel density
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$vessel_density_averaged) Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 4.000 7.000 8.318 11.300 48.000 850 min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity[1] 0cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))
Minimum value number of intraplaque neovessels per 3-4 hotspots: 0.AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels per 3-4 hotspots",
xlab = "natural log-transformed number",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "median",
#add_density = TRUE,
rug = TRUE,
#add.params = list(color = "black", linetype = 2),
title = "number of intraplaque neovessels per 3-4 hotspots",
xlab = "inverse-rank normalized number",
ggtheme = theme_minimal())Using `bins = 30` by default. Pick better value with the argument `bins`.
Binary traits
# calcification
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Calc.bin) no/minor moderate/heavy NA's
1007 850 566 contrasts(AEDB.CEA$Calc.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)
df <- AEDB.CEA %>%
filter(!is.na(CalcificationPlaque)) %>%
group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Calcification",
xlab = "calcification",
ggtheme = theme_minimal())
rm(df)
# collagen
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Collagen.bin) no/minor moderate/heavy NA's
382 1469 572 contrasts(AEDB.CEA$Collagen.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)
df <- AEDB.CEA %>%
filter(!is.na(CollagenPlaque)) %>%
group_by(Gender, CollagenPlaque) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Collagen",
xlab = "collagen",
ggtheme = theme_minimal())
rm(df)
# fat 10%
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Fat.bin_10) <10% >10% NA's
542 1316 565 contrasts(AEDB.CEA$Fat.bin_10) >10%
<10% 0
>10% 1AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)
df <- AEDB.CEA %>%
filter(!is.na(Fat10Perc)) %>%
group_by(Gender, Fat10Perc) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Intraplaque fat",
xlab = "intraplaque fat",
ggtheme = theme_minimal())
rm(df)
# macrophages binned
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$Macrophages.bin) no/minor moderate/heavy NA's
847 992 584 contrasts(AEDB.CEA$Macrophages.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$MAC_binned <- as.factor(AEDB.CEA$Macrophages.bin)
df <- AEDB.CEA %>%
filter(!is.na(MAC_binned)) %>%
group_by(Gender, MAC_binned) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Macrophages (binned)",
xlab = "Macrophages",
ggtheme = theme_minimal())
rm(df)
# macrophages grouped
cat("Summary of data.\n")Summary of data.AEDB.CEA$macrophages <- as.factor(AEDB.CEA$macrophages)
summary(AEDB.CEA$macrophages)-888 0 1 2 3 NA's
6 173 674 786 206 578 contrasts(AEDB.CEA$macrophages) 0 1 2 3
-888 0 0 0 0
0 1 0 0 0
1 0 1 0 0
2 0 0 1 0
3 0 0 0 1AEDB.CEA$MAC_grouped <- as.factor(AEDB.CEA$macrophages)
df <- AEDB.CEA %>%
filter(!is.na(MAC_grouped)) %>%
group_by(Gender, MAC_grouped) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "MAC_grouped", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Macrophages (grouped)",
xlab = "Macrophages",
ggtheme = theme_minimal())
rm(df)
# SMC binned
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$SMC.bin) no/minor moderate/heavy NA's
602 1244 577 contrasts(AEDB.CEA$SMC.bin) moderate/heavy
no/minor 0
moderate/heavy 1AEDB.CEA$SMC_binned <- as.factor(AEDB.CEA$SMC.bin)
df <- AEDB.CEA %>%
filter(!is.na(SMC_binned)) %>%
group_by(Gender, SMC_binned) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "SMC (binned)",
xlab = "SMC",
ggtheme = theme_minimal())
rm(df)
# SMC grouped
cat("Summary of data.\n")Summary of data.AEDB.CEA$smc <- as.factor(AEDB.CEA$smc)
summary(AEDB.CEA$smc)-888 0 1 2 3 NA's
4 44 558 908 336 573 contrasts(AEDB.CEA$smc) 0 1 2 3
-888 0 0 0 0
0 1 0 0 0
1 0 1 0 0
2 0 0 1 0
3 0 0 0 1AEDB.CEA$SMC_grouped <- as.factor(AEDB.CEA$smc)
df <- AEDB.CEA %>%
filter(!is.na(SMC_grouped)) %>%
group_by(Gender, SMC_grouped) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "SMC_grouped", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "SMC (grouped)",
xlab = "SMC",
ggtheme = theme_minimal())
rm(df)
# IPH
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$IPH.bin) no yes NA's
746 1108 569 contrasts(AEDB.CEA$IPH.bin) yes
no 0
yes 1AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)
df <- AEDB.CEA %>%
filter(!is.na(IPH)) %>%
group_by(Gender, IPH) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "IPH", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Intraplaque hemorrhage",
xlab = "intraplaque hemorrhage",
ggtheme = theme_minimal())
rm(df)
# Symptoms
cat("Summary of data.\n")Summary of data.summary(AEDB.CEA$AsymptSympt) Asymptomatic Ocular and others Symptomatic
270 541 1612 contrasts(AEDB.CEA$AsymptSympt) Ocular and others Symptomatic
Asymptomatic 0 0
Ocular and others 1 0
Symptomatic 0 1AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)
df <- AEDB.CEA %>%
filter(!is.na(AsymptSympt)) %>%
group_by(Gender, AsymptSympt) %>%
summarise(counts = n())
ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#DB003F", "#1290D9"),
label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
title = "Symptoms",
xlab = "symptoms",
ggtheme = theme_minimal())
rm(df)
Correlations between MCP1 plaque levels and surgery year
Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).
p1 <- ggpubr::ggscatter(AEDB.CEA,
x = "ORyear",
y = "MCP1_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "year of surgery",
ylab = "experiment 1",
title = "MCP1 plaque levels, INT, [pg/mL]",
ggtheme = theme_minimal())
p1 
p2 <- ggpubr::ggscatter(AEDB.CEA,
x = "ORyear",
y = "MCP1_pg_ml_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "year of surgery",
ylab = "experiment 2, [pg/mL]",
title = "MCP1 plaque levels, INT, [pg/mL]/[pg/ug]",
ggtheme = theme_minimal())
p2 
p3 <- ggpubr::ggscatter(AEDB.CEA,
x = "ORyear",
y = "MCP1_pg_ug_2015_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "year of surgery",
ylab = "experiment 2, [pg/ug]",
title = "MCP1 plaque levels, INT",
ggtheme = theme_minimal())
p3
Correlations between MCP1 plasma levels and surgery year
Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).
library(patchwork)
p1 <- ggpubr::ggscatter(AEDB.CEA,
x = "ORyear",
y = "MCP1_plasma_olink",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "year of surgery",
ylab = "MCP1 plasma - OLINK",
title = "MCP1 plasma, [AU]",
ggtheme = theme_minimal())
p2 <- ggpubr::ggscatter(AEDB.CEA,
x = "ORyear",
y = "MCP1_plasma_olink_rank",
color = "#1290D9",
# fill = "Gender",
# palette = c("#1290D9", "#DB003F"),
add = "reg.line",
add.params = list(color = "black", linetype = 2),
cor.coef = TRUE, cor.method = "spearman",
xlab = "year of surgery",
ylab = "MCP1 plasma - OLINK",
title = "MCP1 plasma, INT, [AU]",
ggtheme = theme_minimal())
p1 / p2
rm(p1, p2)In this section we make some variables to assist with analysis.
AEDB.CEA.samplesize = nrow(AEDB.CEA)
# TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
# TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
# TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_rank")
TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank", "MCP1_rank", "MCP1_plasma_olink_rank")
# TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN")
# TRAITS.CON.RANK = c("Macrophages_rank")
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")
# TRAITS.BIN = c("MAC_binned")
TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
"MAC_binned", "SMC_binned")
# "Hospital",
# "Age", "Gender",
# "TC_final", "LDL_final", "HDL_final", "TG_final",
# "systolic", "diastoli", "GFR_MDRD", "BMI",
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite",
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD",
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin",
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015",
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"
# 1. Age (continuous in 1-year increment). [Age]
# 2. Sex (male vs. female). [Gender]
# 3. Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4. Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5. Smoking (current, ex-, never). [SmokerCurrent]
# 6. LDL-C levels (continuous). [LDL_final]
# 7. Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8. Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9. eGFR (continuous). [GFR_MDRD]
# 10. BMI (continuous). [BMI]
# 11. History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12. Level of stenosis (50-70% vs. 70-99%). [stenose]
# Models
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,
AEDB.CEA$ORdate_epoch <- as.numeric(AEDB.CEA$dateok)
AEDB.CEA$ORdate_year <- as.numeric(year(AEDB.CEA$dateok))
cat("Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`\n")Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`summary(AEDB.CEA$ORdate_epoch) Min. 1st Qu. Median Mean 3rd Qu. Max.
11770 13132 14518 14567 15860 18250 cat("\nSummary of 'year of surgery' in 'years' (); coded as `factor()`\n")
Summary of 'year of surgery' in 'years' (); coded as `factor()`table(AEDB.CEA$ORdate_year)
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
81 157 190 185 183 152 138 182 159 164 176 149 163 76 85 65 66 52 COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
# COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch")
COVARIATES_M2 = c(COVARIATES_M1,
"Hypertension.composite", "DiabetesStatus",
"SmokerStatus",
# "SmokerCurrent",
"Med.Statin.LLD", "Med.all.antiplatelet",
"GFR_MDRD", "BMI",
# "CAD_history", "Stroke_history", "Peripheral.interv",
"MedHx_CVD",
"stenose")
# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")
# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")
# COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
# COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Quantitative plaque traits
Analysis of continuous/quantitative plaque traits as a function of plasma/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
-171.29948 0.08535
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1754 -0.6183 0.0000 0.5768 3.4333
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.766e+02 1.967e+01 -8.977 <2e-16 ***
currentDF[, TRAIT] 2.156e-02 2.939e-02 0.734 0.463
Age -3.224e-03 3.106e-03 -1.038 0.299
Gendermale 8.302e-02 6.147e-02 1.350 0.177
ORdate_year 8.806e-02 9.811e-03 8.976 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.967 on 1165 degrees of freedom
Multiple R-squared: 0.06843, Adjusted R-squared: 0.06523
F-statistic: 21.39 on 4 and 1165 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.021564
Standard error............: 0.029392
Odds ratio (effect size)..: 1.022
Lower 95% CI..............: 0.965
Upper 95% CI..............: 1.082
T-value...................: 0.733661
P-value...................: 0.463303
R^2.......................: 0.068426
Adjusted r^2..............: 0.065227
Sample size of AE DB......: 2423
Sample size of model......: 1170
Missing data %............: 51.71275
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age ORdate_year
-167.30363 -0.05958 -0.00447 0.08351
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1539 -0.6160 -0.0022 0.5685 3.4649
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.675e+02 1.954e+01 -8.575 <2e-16 ***
currentDF[, TRAIT] -5.520e-02 3.045e-02 -1.813 0.0701 .
Age -4.433e-03 3.145e-03 -1.410 0.1589
Gendermale 6.493e-02 6.191e-02 1.049 0.2945
ORdate_year 8.359e-02 9.741e-03 8.581 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9656 on 1161 degrees of freedom
Multiple R-squared: 0.07183, Adjusted R-squared: 0.06864
F-statistic: 22.46 on 4 and 1161 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.055203
Standard error............: 0.030454
Odds ratio (effect size)..: 0.946
Lower 95% CI..............: 0.891
Upper 95% CI..............: 1.004
T-value...................: -1.812656
P-value...................: 0.07014308
R^2.......................: 0.071833
Adjusted r^2..............: 0.068636
Sample size of AE DB......: 2423
Sample size of model......: 1166
Missing data %............: 51.87784
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
-153.89746 -0.08229 0.10388 0.07664
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0878 -0.6348 0.0019 0.5515 3.4172
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.555e+02 2.037e+01 -7.637 4.86e-14 ***
currentDF[, TRAIT] -8.194e-02 3.038e-02 -2.697 0.0071 **
Age -2.515e-03 3.247e-03 -0.774 0.4388
Gendermale 1.042e-01 6.409e-02 1.626 0.1043
ORdate_year 7.754e-02 1.016e-02 7.631 5.08e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9768 on 1089 degrees of freedom
Multiple R-squared: 0.06876, Adjusted R-squared: 0.06534
F-statistic: 20.1 on 4 and 1089 DF, p-value: 5.483e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.081944
Standard error............: 0.030383
Odds ratio (effect size)..: 0.921
Lower 95% CI..............: 0.868
Upper 95% CI..............: 0.978
T-value...................: -2.697016
P-value...................: 0.007104392
R^2.......................: 0.068759
Adjusted r^2..............: 0.065339
Sample size of AE DB......: 2423
Sample size of model......: 1094
Missing data %............: 54.84936
Analysis of MCP1_pg_ml_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
-254.43741 0.06422 0.32420 0.12666
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.98066 -0.58281 -0.01477 0.58485 3.01698
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.535e+02 1.846e+01 -13.732 < 2e-16 ***
currentDF[, TRAIT] 6.552e-02 2.761e-02 2.373 0.0178 *
Age 1.944e-03 2.918e-03 0.666 0.5055
Gendermale 3.236e-01 5.775e-02 5.603 2.63e-08 ***
ORdate_year 1.261e-01 9.207e-03 13.698 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9087 on 1166 degrees of freedom
Multiple R-squared: 0.1648, Adjusted R-squared: 0.162
F-statistic: 57.54 on 4 and 1166 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.065515
Standard error............: 0.027613
Odds ratio (effect size)..: 1.068
Lower 95% CI..............: 1.011
Upper 95% CI..............: 1.127
T-value...................: 2.372657
P-value...................: 0.01782216
R^2.......................: 0.164841
Adjusted r^2..............: 0.161976
Sample size of AE DB......: 2423
Sample size of model......: 1171
Missing data %............: 51.67148
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
-232.0776 -0.0943 0.3013 0.1155
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.15076 -0.58587 -0.02393 0.55488 3.09880
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.322e+02 1.827e+01 -12.713 < 2e-16 ***
currentDF[, TRAIT] -9.502e-02 2.851e-02 -3.333 0.000887 ***
Age -4.616e-04 2.944e-03 -0.157 0.875435
Gendermale 3.012e-01 5.795e-02 5.198 2.38e-07 ***
ORdate_year 1.156e-01 9.109e-03 12.694 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.904 on 1162 degrees of freedom
Multiple R-squared: 0.1708, Adjusted R-squared: 0.168
F-statistic: 59.84 on 4 and 1162 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.095019
Standard error............: 0.02851
Odds ratio (effect size)..: 0.909
Lower 95% CI..............: 0.86
Upper 95% CI..............: 0.962
T-value...................: -3.332864
P-value...................: 0.0008866462
R^2.......................: 0.170817
Adjusted r^2..............: 0.167963
Sample size of AE DB......: 2423
Sample size of model......: 1167
Missing data %............: 51.83657
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
-228.08372 -0.06221 0.33587 0.11352
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.04767 -0.60662 0.00131 0.57990 3.05690
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.268e+02 1.916e+01 -11.838 < 2e-16 ***
currentDF[, TRAIT] -6.247e-02 2.862e-02 -2.183 0.0293 *
Age 1.921e-03 3.059e-03 0.628 0.5302
Gendermale 3.356e-01 6.036e-02 5.561 3.38e-08 ***
ORdate_year 1.128e-01 9.561e-03 11.801 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9201 on 1090 degrees of freedom
Multiple R-squared: 0.1558, Adjusted R-squared: 0.1527
F-statistic: 50.28 on 4 and 1090 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.062472
Standard error............: 0.02862
Odds ratio (effect size)..: 0.939
Lower 95% CI..............: 0.888
Upper 95% CI..............: 0.994
T-value...................: -2.182858
P-value...................: 0.02925907
R^2.......................: 0.155768
Adjusted r^2..............: 0.15267
Sample size of AE DB......: 2423
Sample size of model......: 1095
Missing data %............: 54.80809
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
422.8331 0.1222 0.2600 -0.2111
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4317 -0.6291 -0.0261 0.6543 2.8355
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 414.987584 75.061156 5.529 4.99e-08 ***
currentDF[, TRAIT] 0.121339 0.038035 3.190 0.0015 **
Age -0.006268 0.004724 -1.327 0.1851
Gendermale 0.263235 0.090556 2.907 0.0038 **
ORdate_year -0.206979 0.037471 -5.524 5.13e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9598 on 550 degrees of freedom
Multiple R-squared: 0.0847, Adjusted R-squared: 0.07804
F-statistic: 12.72 on 4 and 550 DF, p-value: 6.54e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.121339
Standard error............: 0.038035
Odds ratio (effect size)..: 1.129
Lower 95% CI..............: 1.048
Upper 95% CI..............: 1.216
T-value...................: 3.19016
P-value...................: 0.001502979
R^2.......................: 0.084699
Adjusted r^2..............: 0.078042
Sample size of AE DB......: 2423
Sample size of model......: 555
Missing data %............: 77.09451
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
485.13156 -0.22645 -0.01251 0.22171 -0.24174
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2040 -0.6017 -0.0439 0.6538 2.7241
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 485.131555 74.828140 6.483 2.01e-10 ***
currentDF[, TRAIT] -0.226449 0.039572 -5.722 1.73e-08 ***
Age -0.012514 0.004716 -2.654 0.0082 **
Gendermale 0.221712 0.089045 2.490 0.0131 *
ORdate_year -0.241741 0.037348 -6.473 2.14e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9371 on 547 degrees of freedom
Multiple R-squared: 0.1219, Adjusted R-squared: 0.1155
F-statistic: 18.98 on 4 and 547 DF, p-value: 1.253e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.226449
Standard error............: 0.039572
Odds ratio (effect size)..: 0.797
Lower 95% CI..............: 0.738
Upper 95% CI..............: 0.862
T-value...................: -5.722399
P-value...................: 1.731767e-08
R^2.......................: 0.121879
Adjusted r^2..............: 0.115458
Sample size of AE DB......: 2423
Sample size of model......: 552
Missing data %............: 77.21832
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
427.9795 0.2941 -0.2137
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4045 -0.5978 -0.0351 0.6466 2.6590
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 407.491047 77.437980 5.262 2.06e-07 ***
currentDF[, TRAIT] -0.055611 0.050753 -1.096 0.27369
Age -0.006762 0.004796 -1.410 0.15917
Gendermale 0.296448 0.092030 3.221 0.00135 **
ORdate_year -0.203215 0.038660 -5.257 2.12e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9662 on 539 degrees of freedom
Multiple R-squared: 0.07477, Adjusted R-squared: 0.0679
F-statistic: 10.89 on 4 and 539 DF, p-value: 1.697e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.055611
Standard error............: 0.050753
Odds ratio (effect size)..: 0.946
Lower 95% CI..............: 0.856
Upper 95% CI..............: 1.045
T-value...................: -1.09571
P-value...................: 0.2736949
R^2.......................: 0.07477
Adjusted r^2..............: 0.067904
Sample size of AE DB......: 2423
Sample size of model......: 544
Missing data %............: 77.54849
Analysis of MCP1_plasma_olink_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Age ORdate_year
67.69277 0.02550 -0.03461
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8535 -0.6687 0.0154 0.6567 3.4821
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.500883 31.153667 2.199 0.0284 *
currentDF[, TRAIT] -0.012239 0.044726 -0.274 0.7845
Age 0.025022 0.004874 5.134 4.09e-07 ***
Gendermale 0.095085 0.094628 1.005 0.3155
ORdate_year -0.035029 0.015539 -2.254 0.0246 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9912 on 496 degrees of freedom
Multiple R-squared: 0.05955, Adjusted R-squared: 0.05197
F-statistic: 7.852 on 4 and 496 DF, p-value: 3.846e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: -0.012239
Standard error............: 0.044726
Odds ratio (effect size)..: 0.988
Lower 95% CI..............: 0.905
Upper 95% CI..............: 1.078
T-value...................: -0.273634
P-value...................: 0.7844798
R^2.......................: 0.05955
Adjusted r^2..............: 0.051966
Sample size of AE DB......: 2423
Sample size of model......: 501
Missing data %............: 79.32315
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Age ORdate_year
70.70209 0.02554 -0.03611
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8529 -0.6753 0.0129 0.6472 3.4913
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.9786513 31.2518311 2.207 0.0278 *
currentDF[, TRAIT] 0.0006227 0.0505420 0.012 0.9902
Age 0.0250800 0.0048962 5.122 4.33e-07 ***
Gendermale 0.0978698 0.0951165 1.029 0.3040
ORdate_year -0.0352682 0.0155823 -2.263 0.0240 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9909 on 494 degrees of freedom
Multiple R-squared: 0.0605, Adjusted R-squared: 0.05289
F-statistic: 7.953 on 4 and 494 DF, p-value: 3.22e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0.000623
Standard error............: 0.050542
Odds ratio (effect size)..: 1.001
Lower 95% CI..............: 0.906
Upper 95% CI..............: 1.105
T-value...................: 0.01232
P-value...................: 0.9901749
R^2.......................: 0.060502
Adjusted r^2..............: 0.052895
Sample size of AE DB......: 2423
Sample size of model......: 499
Missing data %............: 79.40569
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Age ORdate_year
67.32987 0.02446 -0.03439
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8120 -0.6681 0.0078 0.6115 3.4898
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 61.510311 32.117556 1.915 0.0561 .
currentDF[, TRAIT] 0.028090 0.048865 0.575 0.5657
Age 0.023845 0.005171 4.611 5.23e-06 ***
Gendermale 0.113115 0.100258 1.128 0.2598
ORdate_year -0.031510 0.016026 -1.966 0.0499 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9953 on 450 degrees of freedom
Multiple R-squared: 0.05571, Adjusted R-squared: 0.04732
F-statistic: 6.637 on 4 and 450 DF, p-value: 3.388e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: 0.02809
Standard error............: 0.048865
Odds ratio (effect size)..: 1.028
Lower 95% CI..............: 0.935
Upper 95% CI..............: 1.132
T-value...................: 0.574848
P-value...................: 0.5656816
R^2.......................: 0.055712
Adjusted r^2..............: 0.047319
Sample size of AE DB......: 2423
Sample size of model......: 455
Missing data %............: 81.22163 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Binary plaque traits
Analysis of binary plaque traits as a function of plasma/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
335.24007 -0.33036 0.02155 -0.19206 -0.16774
Degrees of Freedom: 1179 Total (i.e. Null); 1175 Residual
Null Deviance: 1635
Residual Deviance: 1509 AIC: 1519
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8849 -1.0513 -0.6462 1.0731 1.9198
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 335.240072 43.279314 7.746 9.49e-15 ***
currentDF[, PROTEIN] -0.330357 0.064530 -5.119 3.07e-07 ***
Age 0.021553 0.006796 3.171 0.00152 **
Gendermale -0.192060 0.133536 -1.438 0.15036
ORdate_year -0.167737 0.021599 -7.766 8.11e-15 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1635.3 on 1179 degrees of freedom
Residual deviance: 1509.4 on 1175 degrees of freedom
AIC: 1519.4
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.330357
Standard error............: 0.06453
Odds ratio (effect size)..: 0.719
Lower 95% CI..............: 0.633
Upper 95% CI..............: 0.816
Z-value...................: -5.1194
P-value...................: 3.065085e-07
Hosmer and Lemeshow r^2...: 0.07695
Cox and Snell r^2.........: 0.101149
Nagelkerke's pseudo r^2...: 0.134888
Sample size of AE DB......: 2423
Sample size of model......: 1180
Missing data %............: 51.30004
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
1.3330 -0.2309
Degrees of Freedom: 1180 Total (i.e. Null); 1179 Residual
Null Deviance: 1217
Residual Deviance: 1206 AIC: 1210
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1021 0.5727 0.6548 0.7158 0.9485
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 17.784713 48.613369 0.366 0.71448
currentDF[, PROTEIN] -0.224125 0.074534 -3.007 0.00264 **
Age 0.003150 0.007836 0.402 0.68767
Gendermale -0.018114 0.156133 -0.116 0.90764
ORdate_year -0.008298 0.024251 -0.342 0.73221
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1216.6 on 1180 degrees of freedom
Residual deviance: 1206.0 on 1176 degrees of freedom
AIC: 1216
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.224125
Standard error............: 0.074534
Odds ratio (effect size)..: 0.799
Lower 95% CI..............: 0.691
Upper 95% CI..............: 0.925
Z-value...................: -3.007008
P-value...................: 0.00263833
Hosmer and Lemeshow r^2...: 0.00871
Cox and Snell r^2.........: 0.008933
Nagelkerke's pseudo r^2...: 0.013891
Sample size of AE DB......: 2423
Sample size of model......: 1181
Missing data %............: 51.25877
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
427.40162 0.37536 0.01846 0.92910 -0.21336
Degrees of Freedom: 1180 Total (i.e. Null); 1176 Residual
Null Deviance: 1388
Residual Deviance: 1265 AIC: 1275
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5628 -1.0106 0.6082 0.8037 1.5384
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 427.401616 51.642839 8.276 < 2e-16 ***
currentDF[, PROTEIN] 0.375361 0.076943 4.878 1.07e-06 ***
Age 0.018459 0.007414 2.490 0.0128 *
Gendermale 0.929096 0.142899 6.502 7.94e-11 ***
ORdate_year -0.213360 0.025759 -8.283 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1387.7 on 1180 degrees of freedom
Residual deviance: 1264.5 on 1176 degrees of freedom
AIC: 1274.5
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.375361
Standard error............: 0.076943
Odds ratio (effect size)..: 1.456
Lower 95% CI..............: 1.252
Upper 95% CI..............: 1.692
Z-value...................: 4.878458
P-value...................: 1.069185e-06
Hosmer and Lemeshow r^2...: 0.088803
Cox and Snell r^2.........: 0.099089
Nagelkerke's pseudo r^2...: 0.143359
Sample size of AE DB......: 2423
Sample size of model......: 1181
Missing data %............: 51.25877
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_year,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
330.7442 0.6639 -0.1648
Degrees of Freedom: 1177 Total (i.e. Null); 1175 Residual
Null Deviance: 1576
Residual Deviance: 1489 AIC: 1495
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8927 -1.1983 0.7379 0.9697 1.6410
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 337.995453 44.297057 7.630 2.34e-14 ***
currentDF[, PROTEIN] 0.005900 0.065633 0.090 0.928
Age 0.009237 0.006770 1.364 0.172
Gendermale 0.662529 0.132839 4.987 6.12e-07 ***
ORdate_year -0.168718 0.022101 -7.634 2.28e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1576.1 on 1177 degrees of freedom
Residual deviance: 1487.0 on 1173 degrees of freedom
AIC: 1497
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.0059
Standard error............: 0.065633
Odds ratio (effect size)..: 1.006
Lower 95% CI..............: 0.884
Upper 95% CI..............: 1.144
Z-value...................: 0.089897
P-value...................: 0.9283688
Hosmer and Lemeshow r^2...: 0.056517
Cox and Snell r^2.........: 0.072828
Nagelkerke's pseudo r^2...: 0.098734
Sample size of AE DB......: 2423
Sample size of model......: 1178
Missing data %............: 51.38258
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year
225.4931 0.1181 0.5871 -0.1125
Degrees of Freedom: 1174 Total (i.e. Null); 1171 Residual
Null Deviance: 1628
Residual Deviance: 1578 AIC: 1586
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6791 -1.1487 0.8701 1.1063 1.5988
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 221.509598 42.104122 5.261 1.43e-07 ***
currentDF[, PROTEIN] 0.116279 0.062576 1.858 0.0631 .
Age -0.006165 0.006543 -0.942 0.3461
Gendermale 0.589375 0.129823 4.540 5.63e-06 ***
ORdate_year -0.110332 0.021003 -5.253 1.50e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1627.9 on 1174 degrees of freedom
Residual deviance: 1576.7 on 1170 degrees of freedom
AIC: 1586.7
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.116279
Standard error............: 0.062576
Odds ratio (effect size)..: 1.123
Lower 95% CI..............: 0.994
Upper 95% CI..............: 1.27
Z-value...................: 1.85822
P-value...................: 0.06313782
Hosmer and Lemeshow r^2...: 0.0314
Cox and Snell r^2.........: 0.042569
Nagelkerke's pseudo r^2...: 0.056776
Sample size of AE DB......: 2423
Sample size of model......: 1175
Missing data %............: 51.5064
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
65.61548 -0.17421 -0.02705 -0.37884 -0.03124
Degrees of Freedom: 1175 Total (i.e. Null); 1171 Residual
Null Deviance: 1469
Residual Deviance: 1434 AIC: 1444
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9218 -1.3467 0.7606 0.8945 1.3086
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 65.615476 43.794115 1.498 0.134063
currentDF[, PROTEIN] -0.174212 0.066806 -2.608 0.009115 **
Age -0.027051 0.007164 -3.776 0.000159 ***
Gendermale -0.378837 0.142483 -2.659 0.007842 **
ORdate_year -0.031244 0.021846 -1.430 0.152656
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1469.3 on 1175 degrees of freedom
Residual deviance: 1433.5 on 1171 degrees of freedom
AIC: 1443.5
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.174212
Standard error............: 0.066806
Odds ratio (effect size)..: 0.84
Lower 95% CI..............: 0.737
Upper 95% CI..............: 0.958
Z-value...................: -2.607727
P-value...................: 0.009114572
Hosmer and Lemeshow r^2...: 0.024381
Cox and Snell r^2.........: 0.030004
Nagelkerke's pseudo r^2...: 0.042061
Sample size of AE DB......: 2423
Sample size of model......: 1176
Missing data %............: 51.46513
Analysis of MCP1_pg_ml_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + ORdate_year, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age ORdate_year
310.77553 -0.34903 0.02309 -0.15567
Degrees of Freedom: 1180 Total (i.e. Null); 1177 Residual
Null Deviance: 1637
Residual Deviance: 1513 AIC: 1521
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8216 -1.0490 -0.6315 1.0837 2.0978
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 312.506897 44.478670 7.026 2.13e-12 ***
currentDF[, PROTEIN] -0.340451 0.068997 -4.934 8.04e-07 ***
Age 0.023128 0.006798 3.402 0.000669 ***
Gendermale -0.109930 0.134724 -0.816 0.414521
ORdate_year -0.156493 0.022190 -7.052 1.76e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1636.6 on 1180 degrees of freedom
Residual deviance: 1512.0 on 1176 degrees of freedom
AIC: 1522
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.340451
Standard error............: 0.068997
Odds ratio (effect size)..: 0.711
Lower 95% CI..............: 0.621
Upper 95% CI..............: 0.814
Z-value...................: -4.934311
P-value...................: 8.04341e-07
Hosmer and Lemeshow r^2...: 0.07616
Cox and Snell r^2.........: 0.100162
Nagelkerke's pseudo r^2...: 0.133573
Sample size of AE DB......: 2423
Sample size of model......: 1181
Missing data %............: 51.25877
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN],
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN]
1.3403 -0.2873
Degrees of Freedom: 1181 Total (i.e. Null); 1180 Residual
Null Deviance: 1217
Residual Deviance: 1202 AIC: 1206
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2054 0.5389 0.6456 0.7150 1.0194
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -17.700869 50.816422 -0.348 0.727593
currentDF[, PROTEIN] -0.304533 0.079925 -3.810 0.000139 ***
Age 0.004359 0.007869 0.554 0.579607
Gendermale 0.066303 0.158551 0.418 0.675814
ORdate_year 0.009316 0.025343 0.368 0.713171
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1217.1 on 1181 degrees of freedom
Residual deviance: 1200.8 on 1177 degrees of freedom
AIC: 1210.8
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.304533
Standard error............: 0.079925
Odds ratio (effect size)..: 0.737
Lower 95% CI..............: 0.631
Upper 95% CI..............: 0.863
Z-value...................: -3.810259
P-value...................: 0.0001388211
Hosmer and Lemeshow r^2...: 0.013321
Cox and Snell r^2.........: 0.013622
Nagelkerke's pseudo r^2...: 0.021189
Sample size of AE DB......: 2423
Sample size of model......: 1182
Missing data %............: 51.2175
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
467.88804 0.44852 0.01646 0.80595 -0.23342
Degrees of Freedom: 1181 Total (i.e. Null); 1177 Residual
Null Deviance: 1390
Residual Deviance: 1258 AIC: 1268
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6456 -0.9830 0.5993 0.7919 1.6326
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 467.888045 53.325556 8.774 < 2e-16 ***
currentDF[, PROTEIN] 0.448516 0.079848 5.617 1.94e-08 ***
Age 0.016457 0.007424 2.217 0.0266 *
Gendermale 0.805951 0.144389 5.582 2.38e-08 ***
ORdate_year -0.233421 0.026589 -8.779 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1390.3 on 1181 degrees of freedom
Residual deviance: 1258.3 on 1177 degrees of freedom
AIC: 1268.3
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.448516
Standard error............: 0.079848
Odds ratio (effect size)..: 1.566
Lower 95% CI..............: 1.339
Upper 95% CI..............: 1.831
Z-value...................: 5.617133
P-value...................: 1.941519e-08
Hosmer and Lemeshow r^2...: 0.094988
Cox and Snell r^2.........: 0.105714
Nagelkerke's pseudo r^2...: 0.152862
Sample size of AE DB......: 2423
Sample size of model......: 1182
Missing data %............: 51.2175
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year
381.3362 0.1826 0.6048 -0.1900
Degrees of Freedom: 1178 Total (i.e. Null); 1175 Residual
Null Deviance: 1578
Residual Deviance: 1483 AIC: 1491
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0163 -1.1800 0.7413 0.9609 1.6978
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 386.982789 46.879353 8.255 < 2e-16 ***
currentDF[, PROTEIN] 0.181469 0.069739 2.602 0.00926 **
Age 0.008978 0.006779 1.324 0.18537
Gendermale 0.603961 0.134706 4.484 7.34e-06 ***
ORdate_year -0.193095 0.023380 -8.259 < 2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1578.0 on 1178 degrees of freedom
Residual deviance: 1481.4 on 1174 degrees of freedom
AIC: 1491.4
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.181469
Standard error............: 0.069739
Odds ratio (effect size)..: 1.199
Lower 95% CI..............: 1.046
Upper 95% CI..............: 1.375
Z-value...................: 2.602126
P-value...................: 0.009264778
Hosmer and Lemeshow r^2...: 0.061186
Cox and Snell r^2.........: 0.078628
Nagelkerke's pseudo r^2...: 0.106581
Sample size of AE DB......: 2423
Sample size of model......: 1179
Missing data %............: 51.34131
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year
261.8955 0.2204 0.5260 -0.1306
Degrees of Freedom: 1175 Total (i.e. Null); 1172 Residual
Null Deviance: 1629
Residual Deviance: 1571 AIC: 1579
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7993 -1.1493 0.8206 1.0983 1.6704
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 258.120368 44.216573 5.838 5.29e-09 ***
currentDF[, PROTEIN] 0.221792 0.066957 3.312 0.000925 ***
Age -0.006995 0.006559 -1.066 0.286220
Gendermale 0.528275 0.131558 4.016 5.93e-05 ***
ORdate_year -0.128524 0.022049 -5.829 5.57e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1629.3 on 1175 degrees of freedom
Residual deviance: 1570.2 on 1171 degrees of freedom
AIC: 1580.2
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.221792
Standard error............: 0.066957
Odds ratio (effect size)..: 1.248
Lower 95% CI..............: 1.095
Upper 95% CI..............: 1.423
Z-value...................: 3.312458
P-value...................: 0.0009247989
Hosmer and Lemeshow r^2...: 0.036292
Cox and Snell r^2.........: 0.049038
Nagelkerke's pseudo r^2...: 0.065402
Sample size of AE DB......: 2423
Sample size of model......: 1176
Missing data %............: 51.46513
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
2.83719 -0.30011 -0.02675 -0.29630
Degrees of Freedom: 1176 Total (i.e. Null); 1173 Residual
Null Deviance: 1470
Residual Deviance: 1425 AIC: 1433
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9616 -1.3190 0.7505 0.8931 1.3158
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 25.048652 45.710531 0.548 0.583703
currentDF[, PROTEIN] -0.287159 0.071979 -3.989 6.62e-05 ***
Age -0.026390 0.007197 -3.667 0.000246 ***
Gendermale -0.299149 0.144473 -2.071 0.038395 *
ORdate_year -0.011078 0.022797 -0.486 0.626996
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1470.1 on 1176 degrees of freedom
Residual deviance: 1425.2 on 1172 degrees of freedom
AIC: 1435.2
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.287159
Standard error............: 0.071979
Odds ratio (effect size)..: 0.75
Lower 95% CI..............: 0.652
Upper 95% CI..............: 0.864
Z-value...................: -3.989465
P-value...................: 6.622254e-05
Hosmer and Lemeshow r^2...: 0.030554
Cox and Snell r^2.........: 0.037444
Nagelkerke's pseudo r^2...: 0.0525
Sample size of AE DB......: 2423
Sample size of model......: 1177
Missing data %............: 51.42386
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) ORdate_year
-451.4488 0.2255
Degrees of Freedom: 555 Total (i.e. Null); 554 Residual
Null Deviance: 749.7
Residual Deviance: 741.7 AIC: 745.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.6532 -1.2833 0.8799 1.0256 1.3391
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -404.18119 165.52876 -2.442 0.0146 *
currentDF[, PROTEIN] -0.09186 0.09068 -1.013 0.3110
Age 0.01195 0.01016 1.176 0.2397
Gendermale -0.15600 0.19729 -0.791 0.4291
ORdate_year 0.20155 0.08263 2.439 0.0147 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 749.67 on 555 degrees of freedom
Residual deviance: 738.30 on 551 degrees of freedom
AIC: 748.3
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.091864
Standard error............: 0.090683
Odds ratio (effect size)..: 0.912
Lower 95% CI..............: 0.764
Upper 95% CI..............: 1.09
Z-value...................: -1.013025
P-value...................: 0.3110484
Hosmer and Lemeshow r^2...: 0.015167
Cox and Snell r^2.........: 0.020242
Nagelkerke's pseudo r^2...: 0.027342
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year
-780.9134 -0.4799 0.3905
Degrees of Freedom: 553 Total (i.e. Null); 551 Residual
Null Deviance: 538
Residual Deviance: 498 AIC: 504
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3527 0.3688 0.5145 0.6775 1.4039
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -801.64188 214.85023 -3.731 0.000191 ***
currentDF[, PROTEIN] -0.48686 0.12206 -3.989 6.65e-05 ***
Age -0.01852 0.01355 -1.367 0.171532
Gendermale -0.14801 0.26231 -0.564 0.572575
ORdate_year 0.40152 0.10726 3.743 0.000182 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 537.98 on 553 degrees of freedom
Residual deviance: 495.64 on 549 degrees of freedom
AIC: 505.64
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.486864
Standard error............: 0.122064
Odds ratio (effect size)..: 0.615
Lower 95% CI..............: 0.484
Upper 95% CI..............: 0.781
Z-value...................: -3.988597
P-value...................: 6.646533e-05
Hosmer and Lemeshow r^2...: 0.0787
Cox and Snell r^2.........: 0.073577
Nagelkerke's pseudo r^2...: 0.118419
Sample size of AE DB......: 2423
Sample size of model......: 554
Missing data %............: 77.13578
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale
1.2197 0.6668 0.5508
Degrees of Freedom: 555 Total (i.e. Null); 553 Residual
Null Deviance: 538.8
Residual Deviance: 497.2 AIC: 503.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4515 0.3661 0.5131 0.6573 1.4042
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.478e+02 2.145e+02 -1.156 0.248
currentDF[, PROTEIN] 6.953e-01 1.238e-01 5.617 1.94e-08 ***
Age 4.338e-03 1.299e-02 0.334 0.738
Gendermale 5.263e-01 2.365e-01 2.226 0.026 *
ORdate_year 1.242e-01 1.071e-01 1.160 0.246
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 538.82 on 555 degrees of freedom
Residual deviance: 495.66 on 551 degrees of freedom
AIC: 505.66
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.695328
Standard error............: 0.123786
Odds ratio (effect size)..: 2.004
Lower 95% CI..............: 1.573
Upper 95% CI..............: 2.555
Z-value...................: 5.617164
P-value...................: 1.941174e-08
Hosmer and Lemeshow r^2...: 0.080102
Cox and Snell r^2.........: 0.07469
Nagelkerke's pseudo r^2...: 0.120356
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale
-0.76646 0.02073 0.78990
Degrees of Freedom: 555 Total (i.e. Null); 553 Residual
Null Deviance: 611.8
Residual Deviance: 594.4 AIC: 600.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0029 0.5582 0.6468 0.7206 1.1969
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 15.339755 191.148455 0.080 0.936038
currentDF[, PROTEIN] 0.064053 0.104480 0.613 0.539831
Age 0.021375 0.011622 1.839 0.065905 .
Gendermale 0.774693 0.212151 3.652 0.000261 ***
ORdate_year -0.008053 0.095422 -0.084 0.932743
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 611.78 on 555 degrees of freedom
Residual deviance: 593.99 on 551 degrees of freedom
AIC: 603.99
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: 0.064053
Standard error............: 0.10448
Odds ratio (effect size)..: 1.066
Lower 95% CI..............: 0.869
Upper 95% CI..............: 1.308
Z-value...................: 0.613068
P-value...................: 0.5398311
Hosmer and Lemeshow r^2...: 0.029089
Cox and Snell r^2.........: 0.031501
Nagelkerke's pseudo r^2...: 0.047211
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year
-823.9069 0.3857 0.3390 0.4112
Degrees of Freedom: 551 Total (i.e. Null); 548 Residual
Null Deviance: 749.1
Residual Deviance: 711.3 AIC: 719.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9353 -1.1973 0.7687 1.0163 1.6249
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -839.30600 175.54024 -4.781 1.74e-06 ***
currentDF[, PROTEIN] 0.37956 0.09495 3.998 6.40e-05 ***
Age -0.01358 0.01043 -1.302 0.1928
Gendermale 0.34867 0.19858 1.756 0.0791 .
ORdate_year 0.41935 0.08763 4.785 1.71e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 749.15 on 551 degrees of freedom
Residual deviance: 709.62 on 547 degrees of freedom
AIC: 719.62
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.379557
Standard error............: 0.094948
Odds ratio (effect size)..: 1.462
Lower 95% CI..............: 1.213
Upper 95% CI..............: 1.761
Z-value...................: 3.997506
P-value...................: 6.401333e-05
Hosmer and Lemeshow r^2...: 0.052768
Cox and Snell r^2.........: 0.06911
Nagelkerke's pseudo r^2...: 0.093064
Sample size of AE DB......: 2423
Sample size of model......: 552
Missing data %............: 77.21832
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
-331.25779 -0.44085 -0.03875 -0.59679 0.16731
Degrees of Freedom: 552 Total (i.e. Null); 548 Residual
Null Deviance: 667.1
Residual Deviance: 622.7 AIC: 632.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1704 -1.2187 0.6567 0.8334 1.4336
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -331.25779 184.63912 -1.794 0.07280 .
currentDF[, PROTEIN] -0.44085 0.10541 -4.182 2.88e-05 ***
Age -0.03875 0.01183 -3.276 0.00105 **
Gendermale -0.59679 0.23370 -2.554 0.01066 *
ORdate_year 0.16731 0.09218 1.815 0.06952 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 667.10 on 552 degrees of freedom
Residual deviance: 622.68 on 548 degrees of freedom
AIC: 632.68
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.440853
Standard error............: 0.105406
Odds ratio (effect size)..: 0.643
Lower 95% CI..............: 0.523
Upper 95% CI..............: 0.791
Z-value...................: -4.182437
P-value...................: 2.88401e-05
Hosmer and Lemeshow r^2...: 0.066596
Cox and Snell r^2.........: 0.077195
Nagelkerke's pseudo r^2...: 0.110168
Sample size of AE DB......: 2423
Sample size of model......: 553
Missing data %............: 77.17705
Analysis of MCP1_plasma_olink_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_year,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age ORdate_year
269.97365 0.02205 -0.13534
Degrees of Freedom: 549 Total (i.e. Null); 547 Residual
Null Deviance: 755.5
Residual Deviance: 727.5 AIC: 733.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5320 -1.0391 -0.7965 1.1898 1.7303
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 273.51943 55.19615 4.955 7.22e-07 ***
currentDF[, PROTEIN] -0.03494 0.08978 -0.389 0.6971
Age 0.02354 0.01006 2.340 0.0193 *
Gendermale -0.14575 0.18861 -0.773 0.4397
ORdate_year -0.13711 0.02756 -4.975 6.52e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 755.46 on 549 degrees of freedom
Residual deviance: 726.70 on 545 degrees of freedom
AIC: 736.7
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.034943
Standard error............: 0.089781
Odds ratio (effect size)..: 0.966
Lower 95% CI..............: 0.81
Upper 95% CI..............: 1.151
Z-value...................: -0.389206
P-value...................: 0.6971234
Hosmer and Lemeshow r^2...: 0.03807
Cox and Snell r^2.........: 0.050947
Nagelkerke's pseudo r^2...: 0.068221
Sample size of AE DB......: 2423
Sample size of model......: 550
Missing data %............: 77.30087
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age
-0.35865 0.02119
Degrees of Freedom: 547 Total (i.e. Null); 546 Residual
Null Deviance: 614.1
Residual Deviance: 610.2 AIC: 614.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8817 0.6079 0.7110 0.7762 1.0162
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 52.54596 60.75132 0.865 0.3871
currentDF[, PROTEIN] 0.05692 0.10123 0.562 0.5739
Age 0.02158 0.01118 1.930 0.0536 .
Gendermale -0.09615 0.21428 -0.449 0.6536
ORdate_year -0.02633 0.03031 -0.869 0.3851
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 614.11 on 547 degrees of freedom
Residual deviance: 608.85 on 543 degrees of freedom
AIC: 618.85
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.05692
Standard error............: 0.101232
Odds ratio (effect size)..: 1.059
Lower 95% CI..............: 0.868
Upper 95% CI..............: 1.291
Z-value...................: 0.562272
P-value...................: 0.5739306
Hosmer and Lemeshow r^2...: 0.008569
Cox and Snell r^2.........: 0.009557
Nagelkerke's pseudo r^2...: 0.014181
Sample size of AE DB......: 2423
Sample size of model......: 548
Missing data %............: 77.38341
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
111.80484 -0.14944 0.02229 0.87302 -0.05626
Degrees of Freedom: 549 Total (i.e. Null); 545 Residual
Null Deviance: 652.2
Residual Deviance: 625.1 AIC: 635.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9812 -1.2413 0.6822 0.8022 1.3509
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 111.80484 60.03007 1.862 0.0625 .
currentDF[, PROTEIN] -0.14944 0.10006 -1.493 0.1353
Age 0.02229 0.01088 2.049 0.0405 *
Gendermale 0.87302 0.19980 4.370 1.25e-05 ***
ORdate_year -0.05626 0.02996 -1.878 0.0604 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 652.25 on 549 degrees of freedom
Residual deviance: 625.11 on 545 degrees of freedom
AIC: 635.11
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: Fat10Perc
Effect size...............: -0.149435
Standard error............: 0.100059
Odds ratio (effect size)..: 0.861
Lower 95% CI..............: 0.708
Upper 95% CI..............: 1.048
Z-value...................: -1.493464
P-value...................: 0.1353157
Hosmer and Lemeshow r^2...: 0.041606
Cox and Snell r^2.........: 0.048143
Nagelkerke's pseudo r^2...: 0.069318
Sample size of AE DB......: 2423
Sample size of model......: 550
Missing data %............: 77.30087
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_year,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
182.76087 0.58346 -0.09097
Degrees of Freedom: 548 Total (i.e. Null); 546 Residual
Null Deviance: 731.4
Residual Deviance: 710.4 AIC: 716.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7203 -1.2967 0.7858 1.0002 1.4155
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.804e+02 5.530e+01 3.263 0.00110 **
currentDF[, PROTEIN] 5.079e-02 9.099e-02 0.558 0.57672
Age -4.181e-04 1.007e-02 -0.042 0.96687
Gendermale 5.780e-01 1.886e-01 3.065 0.00218 **
ORdate_year -8.980e-02 2.760e-02 -3.254 0.00114 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 731.43 on 548 degrees of freedom
Residual deviance: 710.05 on 544 degrees of freedom
AIC: 720.05
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IPH
Effect size...............: 0.05079
Standard error............: 0.090992
Odds ratio (effect size)..: 1.052
Lower 95% CI..............: 0.88
Upper 95% CI..............: 1.258
Z-value...................: 0.558177
P-value...................: 0.5767237
Hosmer and Lemeshow r^2...: 0.029228
Cox and Snell r^2.........: 0.038191
Nagelkerke's pseudo r^2...: 0.051881
Sample size of AE DB......: 2423
Sample size of model......: 549
Missing data %............: 77.34214
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_year,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
134.63657 0.68276 -0.06721
Degrees of Freedom: 544 Total (i.e. Null); 542 Residual
Null Deviance: 753
Residual Deviance: 733.1 AIC: 739.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.5185 -1.2141 0.8996 1.0816 1.5033
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 133.774619 54.125836 2.472 0.013453 *
currentDF[, PROTEIN] 0.004627 0.088814 0.052 0.958453
Age -0.000919 0.009896 -0.093 0.926010
Gendermale 0.683643 0.187768 3.641 0.000272 ***
ORdate_year -0.066752 0.027010 -2.471 0.013457 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 753.02 on 544 degrees of freedom
Residual deviance: 733.07 on 540 degrees of freedom
AIC: 743.07
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.004627
Standard error............: 0.088814
Odds ratio (effect size)..: 1.005
Lower 95% CI..............: 0.844
Upper 95% CI..............: 1.196
Z-value...................: 0.052095
P-value...................: 0.9584528
Hosmer and Lemeshow r^2...: 0.026485
Cox and Snell r^2.........: 0.035932
Nagelkerke's pseudo r^2...: 0.047983
Sample size of AE DB......: 2423
Sample size of model......: 545
Missing data %............: 77.50722
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
ORdate_year, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age Gendermale ORdate_year
106.25670 -0.01859 -0.29266 -0.05181
Degrees of Freedom: 544 Total (i.e. Null); 541 Residual
Null Deviance: 690
Residual Deviance: 679.7 AIC: 687.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8009 -1.3637 0.8035 0.9115 1.1801
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 107.42837 56.90606 1.888 0.0591 .
currentDF[, PROTEIN] -0.01972 0.09305 -0.212 0.8321
Age -0.01811 0.01059 -1.710 0.0873 .
Gendermale -0.29059 0.20168 -1.441 0.1496
ORdate_year -0.05241 0.02839 -1.846 0.0649 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 690.04 on 544 degrees of freedom
Residual deviance: 679.64 on 540 degrees of freedom
AIC: 689.64
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.019725
Standard error............: 0.093051
Odds ratio (effect size)..: 0.98
Lower 95% CI..............: 0.817
Upper 95% CI..............: 1.177
Z-value...................: -0.211976
P-value...................: 0.8321257
Hosmer and Lemeshow r^2...: 0.015072
Cox and Snell r^2.........: 0.018902
Nagelkerke's pseudo r^2...: 0.026323
Sample size of AE DB......: 2423
Sample size of model......: 545
Missing data %............: 77.50722 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Quantitative plaque traits
Analysis of continuous/quantitative plaque traits as a function of plasma/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.CON.RANK)) {
TRAIT = TRAITS.CON.RANK[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + Hypertension.composite +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) ORdate_year Hypertension.compositeyes Med.Statin.LLDyes
-180.14402 0.08994 -0.19984 -0.21274
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1475 -0.6379 -0.0158 0.5706 3.2823
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.911e+02 2.227e+01 -8.583 <2e-16 ***
currentDF[, TRAIT] 1.154e-02 3.180e-02 0.363 0.7167
Age -6.016e-03 3.820e-03 -1.575 0.1156
Gendermale 8.529e-02 6.835e-02 1.248 0.2124
ORdate_year 9.548e-02 1.111e-02 8.597 <2e-16 ***
Hypertension.compositeyes -1.979e-01 9.301e-02 -2.127 0.0336 *
DiabetesStatusDiabetes -3.141e-02 7.459e-02 -0.421 0.6738
SmokerStatusEx-smoker -3.458e-02 7.046e-02 -0.491 0.6237
SmokerStatusNever smoked 1.009e-01 9.950e-02 1.014 0.3110
Med.Statin.LLDyes -2.334e-01 7.534e-02 -3.098 0.0020 **
Med.all.antiplateletyes 5.232e-03 1.049e-01 0.050 0.9602
GFR_MDRD -1.603e-03 1.624e-03 -0.987 0.3238
BMI -1.821e-03 8.518e-03 -0.214 0.8308
MedHx_CVDyes 2.095e-02 6.401e-02 0.327 0.7435
stenose50-70% 2.949e-01 4.165e-01 0.708 0.4791
stenose70-90% 4.026e-01 3.998e-01 1.007 0.3141
stenose90-99% 3.865e-01 4.004e-01 0.965 0.3346
stenose100% (Occlusion) -1.085e-02 5.140e-01 -0.021 0.9832
stenose50-99% 1.365e-01 6.272e-01 0.218 0.8277
stenose70-99% 6.859e-02 5.627e-01 0.122 0.9030
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9674 on 1001 degrees of freedom
Multiple R-squared: 0.08998, Adjusted R-squared: 0.07271
F-statistic: 5.209 on 19 and 1001 DF, p-value: 3.957e-12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.011541
Standard error............: 0.031798
Odds ratio (effect size)..: 1.012
Lower 95% CI..............: 0.95
Upper 95% CI..............: 1.077
T-value...................: 0.36295
P-value...................: 0.7167188
R^2.......................: 0.089983
Adjusted r^2..............: 0.07271
Sample size of AE DB......: 2423
Sample size of model......: 1021
Missing data %............: 57.86215
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age ORdate_year Hypertension.compositeyes
-1.764e+02 -5.324e-02 -5.359e-03 8.828e-02 -1.827e-01
Med.Statin.LLDyes
-2.244e-01
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0767 -0.6241 -0.0146 0.5692 3.3052
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.828e+02 2.203e+01 -8.298 3.41e-16 ***
currentDF[, TRAIT] -4.598e-02 3.259e-02 -1.411 0.15859
Age -6.802e-03 3.852e-03 -1.766 0.07775 .
Gendermale 6.815e-02 6.940e-02 0.982 0.32637
ORdate_year 9.138e-02 1.099e-02 8.316 2.97e-16 ***
Hypertension.compositeyes -1.931e-01 9.317e-02 -2.073 0.03844 *
DiabetesStatusDiabetes -3.052e-02 7.469e-02 -0.409 0.68290
SmokerStatusEx-smoker -3.282e-02 7.067e-02 -0.464 0.64245
SmokerStatusNever smoked 9.394e-02 9.961e-02 0.943 0.34589
Med.Statin.LLDyes -2.296e-01 7.539e-02 -3.046 0.00238 **
Med.all.antiplateletyes -2.309e-04 1.050e-01 -0.002 0.99825
GFR_MDRD -1.493e-03 1.629e-03 -0.917 0.35959
BMI -2.119e-03 8.540e-03 -0.248 0.80413
MedHx_CVDyes 1.909e-02 6.419e-02 0.297 0.76617
stenose50-70% 3.134e-01 4.170e-01 0.752 0.45246
stenose70-90% 4.228e-01 4.003e-01 1.056 0.29113
stenose90-99% 4.078e-01 4.009e-01 1.017 0.30936
stenose100% (Occlusion) 4.733e-03 5.146e-01 0.009 0.99266
stenose50-99% 1.795e-01 6.284e-01 0.286 0.77517
stenose70-99% 1.129e-01 5.635e-01 0.200 0.84127
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9682 on 997 degrees of freedom
Multiple R-squared: 0.09129, Adjusted R-squared: 0.07397
F-statistic: 5.271 on 19 and 997 DF, p-value: 2.547e-12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.045976
Standard error............: 0.032586
Odds ratio (effect size)..: 0.955
Lower 95% CI..............: 0.896
Upper 95% CI..............: 1.018
T-value...................: -1.410893
P-value...................: 0.158588
R^2.......................: 0.091289
Adjusted r^2..............: 0.073971
Sample size of AE DB......: 2423
Sample size of model......: 1017
Missing data %............: 58.02724
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Hypertension.compositeyes Med.Statin.LLDyes
-161.76734 -0.09749 0.08078 -0.18405 -0.21840
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0450 -0.6534 -0.0088 0.5645 3.3553
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.691e+02 2.282e+01 -7.412 2.78e-13 ***
currentDF[, TRAIT] -9.684e-02 3.286e-02 -2.947 0.00329 **
Age -5.379e-03 3.988e-03 -1.349 0.17765
Gendermale 9.656e-02 7.121e-02 1.356 0.17544
ORdate_year 8.451e-02 1.139e-02 7.423 2.58e-13 ***
Hypertension.compositeyes -1.893e-01 9.719e-02 -1.948 0.05171 .
DiabetesStatusDiabetes -4.055e-02 7.964e-02 -0.509 0.61082
SmokerStatusEx-smoker -3.344e-02 7.379e-02 -0.453 0.65053
SmokerStatusNever smoked 9.413e-02 1.041e-01 0.904 0.36606
Med.Statin.LLDyes -2.326e-01 7.796e-02 -2.983 0.00293 **
Med.all.antiplateletyes 2.414e-02 1.113e-01 0.217 0.82831
GFR_MDRD -1.981e-03 1.706e-03 -1.161 0.24585
BMI -7.099e-04 8.901e-03 -0.080 0.93645
MedHx_CVDyes 1.338e-02 6.682e-02 0.200 0.84129
stenose50-70% 2.358e-01 4.592e-01 0.514 0.60770
stenose70-90% 3.639e-01 4.419e-01 0.824 0.41039
stenose90-99% 3.389e-01 4.419e-01 0.767 0.44337
stenose100% (Occlusion) -6.102e-02 5.487e-01 -0.111 0.91146
stenose50-99% 2.443e-01 6.583e-01 0.371 0.71059
stenose70-99% -1.601e-01 6.593e-01 -0.243 0.80823
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9766 on 933 degrees of freedom
Multiple R-squared: 0.09328, Adjusted R-squared: 0.07482
F-statistic: 5.052 on 19 and 933 DF, p-value: 1.356e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.096838
Standard error............: 0.032861
Odds ratio (effect size)..: 0.908
Lower 95% CI..............: 0.851
Upper 95% CI..............: 0.968
T-value...................: -2.946925
P-value...................: 0.003289388
R^2.......................: 0.093282
Adjusted r^2..............: 0.074817
Sample size of AE DB......: 2423
Sample size of model......: 953
Missing data %............: 60.66859
Analysis of MCP1_pg_ml_2015_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
-260.49868 0.06069 0.29422 0.12984 -0.13333
Med.Statin.LLDyes
-0.21052
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.90020 -0.57577 -0.02735 0.60041 3.00874
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.635e+02 2.097e+01 -12.563 < 2e-16 ***
currentDF[, TRAIT] 5.919e-02 2.995e-02 1.977 0.04835 *
Age 1.825e-03 3.597e-03 0.507 0.61205
Gendermale 3.196e-01 6.437e-02 4.964 8.1e-07 ***
ORdate_year 1.313e-01 1.046e-02 12.553 < 2e-16 ***
Hypertension.compositeyes -1.348e-01 8.759e-02 -1.539 0.12424
DiabetesStatusDiabetes -4.051e-02 7.024e-02 -0.577 0.56426
SmokerStatusEx-smoker -5.669e-02 6.636e-02 -0.854 0.39319
SmokerStatusNever smoked 2.783e-02 9.370e-02 0.297 0.76651
Med.Statin.LLDyes -2.103e-01 7.096e-02 -2.964 0.00311 **
Med.all.antiplateletyes 6.384e-02 9.880e-02 0.646 0.51833
GFR_MDRD -4.344e-04 1.529e-03 -0.284 0.77647
BMI -2.830e-03 8.022e-03 -0.353 0.72432
MedHx_CVDyes 5.212e-03 6.028e-02 0.086 0.93111
stenose50-70% -1.761e-01 3.922e-01 -0.449 0.65353
stenose70-90% 4.823e-03 3.765e-01 0.013 0.98978
stenose90-99% -3.883e-02 3.770e-01 -0.103 0.91800
stenose100% (Occlusion) -2.378e-01 4.841e-01 -0.491 0.62344
stenose50-99% -4.599e-01 5.907e-01 -0.779 0.43637
stenose70-99% -3.348e-01 5.299e-01 -0.632 0.52770
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.911 on 1001 degrees of freedom
Multiple R-squared: 0.1751, Adjusted R-squared: 0.1595
F-statistic: 11.19 on 19 and 1001 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.059193
Standard error............: 0.029945
Odds ratio (effect size)..: 1.061
Lower 95% CI..............: 1.001
Upper 95% CI..............: 1.125
T-value...................: 1.976704
P-value...................: 0.04834925
R^2.......................: 0.175135
Adjusted r^2..............: 0.159478
Sample size of AE DB......: 2423
Sample size of model......: 1021
Missing data %............: 57.86215
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
-235.47131 -0.09684 0.26854 0.11737 -0.13276
Med.Statin.LLDyes
-0.19071
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.06717 -0.59118 -0.01529 0.56640 3.08389
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.401e+02 2.070e+01 -11.601 < 2e-16 ***
currentDF[, TRAIT] -9.359e-02 3.061e-02 -3.058 0.00229 **
Age -8.914e-05 3.618e-03 -0.025 0.98035
Gendermale 2.930e-01 6.519e-02 4.494 7.8e-06 ***
ORdate_year 1.197e-01 1.032e-02 11.598 < 2e-16 ***
Hypertension.compositeyes -1.246e-01 8.751e-02 -1.424 0.15478
DiabetesStatusDiabetes -4.013e-02 7.016e-02 -0.572 0.56745
SmokerStatusEx-smoker -5.388e-02 6.638e-02 -0.812 0.41716
SmokerStatusNever smoked 2.007e-02 9.357e-02 0.214 0.83020
Med.Statin.LLDyes -1.952e-01 7.081e-02 -2.756 0.00595 **
Med.all.antiplateletyes 4.912e-02 9.867e-02 0.498 0.61872
GFR_MDRD -1.574e-04 1.530e-03 -0.103 0.91809
BMI -3.457e-03 8.022e-03 -0.431 0.66658
MedHx_CVDyes 2.805e-03 6.029e-02 0.047 0.96290
stenose50-70% -1.347e-01 3.917e-01 -0.344 0.73094
stenose70-90% 5.330e-02 3.760e-01 0.142 0.88731
stenose90-99% 7.963e-03 3.766e-01 0.021 0.98313
stenose100% (Occlusion) -2.181e-01 4.833e-01 -0.451 0.65191
stenose50-99% -3.620e-01 5.902e-01 -0.613 0.53980
stenose70-99% -2.212e-01 5.293e-01 -0.418 0.67615
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9094 on 997 degrees of freedom
Multiple R-squared: 0.1788, Adjusted R-squared: 0.1631
F-statistic: 11.42 on 19 and 997 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.093585
Standard error............: 0.030608
Odds ratio (effect size)..: 0.911
Lower 95% CI..............: 0.858
Upper 95% CI..............: 0.967
T-value...................: -3.057532
P-value...................: 0.002291186
R^2.......................: 0.17877
Adjusted r^2..............: 0.16312
Sample size of AE DB......: 2423
Sample size of model......: 1017
Missing data %............: 58.02724
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
-232.26066 -0.07525 0.29943 0.11576 -0.12715
Med.Statin.LLDyes
-0.21061
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.96576 -0.59822 -0.00485 0.58684 3.01447
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.349e+02 2.150e+01 -10.925 < 2e-16 ***
currentDF[, TRAIT] -7.728e-02 3.096e-02 -2.496 0.01273 *
Age 1.892e-03 3.757e-03 0.504 0.61463
Gendermale 3.218e-01 6.710e-02 4.796 1.88e-06 ***
ORdate_year 1.171e-01 1.073e-02 10.912 < 2e-16 ***
Hypertension.compositeyes -1.342e-01 9.157e-02 -1.466 0.14306
DiabetesStatusDiabetes -5.770e-02 7.505e-02 -0.769 0.44220
SmokerStatusEx-smoker -4.766e-02 6.953e-02 -0.685 0.49321
SmokerStatusNever smoked 7.042e-03 9.807e-02 0.072 0.94278
Med.Statin.LLDyes -2.113e-01 7.346e-02 -2.877 0.00411 **
Med.all.antiplateletyes 7.706e-02 1.049e-01 0.735 0.46265
GFR_MDRD -6.724e-04 1.607e-03 -0.418 0.67581
BMI -2.815e-04 8.387e-03 -0.034 0.97323
MedHx_CVDyes 1.458e-02 6.296e-02 0.232 0.81686
stenose50-70% -2.957e-01 4.327e-01 -0.683 0.49451
stenose70-90% -5.852e-02 4.163e-01 -0.141 0.88824
stenose90-99% -1.188e-01 4.164e-01 -0.285 0.77550
stenose100% (Occlusion) -3.299e-01 5.170e-01 -0.638 0.52349
stenose50-99% -3.890e-01 6.203e-01 -0.627 0.53068
stenose70-99% -5.688e-01 6.212e-01 -0.916 0.36010
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9202 on 933 degrees of freedom
Multiple R-squared: 0.17, Adjusted R-squared: 0.1531
F-statistic: 10.06 on 19 and 933 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.077283
Standard error............: 0.030963
Odds ratio (effect size)..: 0.926
Lower 95% CI..............: 0.871
Upper 95% CI..............: 0.984
T-value...................: -2.496001
P-value...................: 0.01273189
R^2.......................: 0.169991
Adjusted r^2..............: 0.153088
Sample size of AE DB......: 2423
Sample size of model......: 953
Missing data %............: 60.66859
Analysis of MCP1_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
441.5668 0.1036 0.2776 -0.2203 -0.2432
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3192 -0.6253 0.0206 0.6596 2.6344
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.154e+02 8.330e+01 4.987 8.60e-07 ***
currentDF[, TRAIT] 9.996e-02 4.118e-02 2.427 0.01558 *
Age -9.280e-03 5.771e-03 -1.608 0.10849
Gendermale 3.003e-01 1.002e-01 2.997 0.00287 **
ORdate_year -2.067e-01 4.157e-02 -4.972 9.23e-07 ***
Hypertension.compositeyes -2.382e-01 1.329e-01 -1.791 0.07385 .
DiabetesStatusDiabetes -6.961e-02 1.124e-01 -0.619 0.53601
SmokerStatusEx-smoker 8.372e-02 9.983e-02 0.839 0.40209
SmokerStatusNever smoked 2.684e-01 1.476e-01 1.819 0.06960 .
Med.Statin.LLDyes -1.509e-01 1.035e-01 -1.457 0.14568
Med.all.antiplateletyes 1.368e-01 1.587e-01 0.862 0.38929
GFR_MDRD -1.657e-04 2.489e-03 -0.067 0.94696
BMI -1.297e-02 1.190e-02 -1.090 0.27621
MedHx_CVDyes 2.265e-02 9.344e-02 0.242 0.80855
stenose50-70% -4.499e-01 6.185e-01 -0.727 0.46738
stenose70-90% -2.733e-01 5.744e-01 -0.476 0.63444
stenose90-99% -2.510e-01 5.728e-01 -0.438 0.66144
stenose100% (Occlusion) -9.705e-01 7.264e-01 -1.336 0.18217
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9725 on 479 degrees of freedom
Multiple R-squared: 0.1108, Adjusted R-squared: 0.0792
F-statistic: 3.51 on 17 and 479 DF, p-value: 3.139e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.099963
Standard error............: 0.041184
Odds ratio (effect size)..: 1.105
Lower 95% CI..............: 1.019
Upper 95% CI..............: 1.198
T-value...................: 2.427249
P-value...................: 0.01558152
R^2.......................: 0.110762
Adjusted r^2..............: 0.079203
Sample size of AE DB......: 2423
Sample size of model......: 497
Missing data %............: 79.48824
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
511.09348 -0.22506 -0.01132 0.23728 -0.25465
Hypertension.compositeyes
-0.19903
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1076 -0.6197 -0.0034 0.6938 2.4632
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.972e+02 8.244e+01 6.031 3.26e-09 ***
currentDF[, TRAIT] -2.277e-01 4.248e-02 -5.361 1.29e-07 ***
Age -1.495e-02 5.694e-03 -2.626 0.00891 **
Gendermale 2.430e-01 9.865e-02 2.463 0.01411 *
ORdate_year -2.474e-01 4.114e-02 -6.013 3.63e-09 ***
Hypertension.compositeyes -2.009e-01 1.285e-01 -1.563 0.11877
DiabetesStatusDiabetes -7.173e-02 1.095e-01 -0.655 0.51256
SmokerStatusEx-smoker 1.214e-01 9.718e-02 1.249 0.21228
SmokerStatusNever smoked 2.460e-01 1.435e-01 1.714 0.08712 .
Med.Statin.LLDyes -1.412e-01 1.011e-01 -1.397 0.16307
Med.all.antiplateletyes 1.136e-01 1.545e-01 0.735 0.46259
GFR_MDRD 2.431e-05 2.423e-03 0.010 0.99200
BMI -1.152e-02 1.157e-02 -0.996 0.31990
MedHx_CVDyes 1.966e-02 9.120e-02 0.216 0.82941
stenose50-70% -3.893e-01 6.021e-01 -0.647 0.51816
stenose70-90% -2.757e-01 5.591e-01 -0.493 0.62215
stenose90-99% -2.863e-01 5.573e-01 -0.514 0.60768
stenose100% (Occlusion) -1.135e+00 7.069e-01 -1.605 0.10909
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9465 on 477 degrees of freedom
Multiple R-squared: 0.1508, Adjusted R-squared: 0.1206
F-statistic: 4.983 on 17 and 477 DF, p-value: 5.352e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_rank
Effect size...............: -0.22772
Standard error............: 0.042481
Odds ratio (effect size)..: 0.796
Lower 95% CI..............: 0.733
Upper 95% CI..............: 0.865
T-value...................: -5.360554
P-value...................: 1.29467e-07
R^2.......................: 0.150815
Adjusted r^2..............: 0.12055
Sample size of AE DB......: 2423
Sample size of model......: 495
Missing data %............: 79.57078
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
463.7343 0.3103 -0.2315
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3250 -0.6474 0.0106 0.6218 2.5297
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.218e+02 8.620e+01 4.893 1.37e-06 ***
currentDF[, TRAIT] -4.850e-02 5.471e-02 -0.887 0.3758
Age -9.513e-03 5.860e-03 -1.623 0.1052
Gendermale 3.285e-01 1.016e-01 3.235 0.0013 **
ORdate_year -2.100e-01 4.302e-02 -4.880 1.46e-06 ***
Hypertension.compositeyes -1.645e-01 1.355e-01 -1.214 0.2254
DiabetesStatusDiabetes -3.384e-02 1.148e-01 -0.295 0.7683
SmokerStatusEx-smoker 9.358e-02 1.014e-01 0.923 0.3567
SmokerStatusNever smoked 2.664e-01 1.497e-01 1.780 0.0758 .
Med.Statin.LLDyes -1.516e-01 1.052e-01 -1.442 0.1500
Med.all.antiplateletyes 1.357e-01 1.616e-01 0.840 0.4015
GFR_MDRD 6.123e-04 2.560e-03 0.239 0.8111
BMI -1.108e-02 1.208e-02 -0.917 0.3595
MedHx_CVDyes 3.668e-02 9.504e-02 0.386 0.6997
stenose50-70% -5.382e-01 6.214e-01 -0.866 0.3869
stenose70-90% -2.824e-01 5.775e-01 -0.489 0.6251
stenose90-99% -2.830e-01 5.758e-01 -0.491 0.6233
stenose100% (Occlusion) -1.043e+00 7.302e-01 -1.429 0.1538
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9771 on 469 degrees of freedom
Multiple R-squared: 0.1035, Adjusted R-squared: 0.07105
F-statistic: 3.186 on 17 and 469 DF, p-value: 2.002e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.048502
Standard error............: 0.054711
Odds ratio (effect size)..: 0.953
Lower 95% CI..............: 0.856
Upper 95% CI..............: 1.06
T-value...................: -0.886513
P-value...................: 0.3757955
R^2.......................: 0.103542
Adjusted r^2..............: 0.071048
Sample size of AE DB......: 2423
Sample size of model......: 487
Missing data %............: 79.90095
Analysis of MCP1_plasma_olink_rank.
- processing Macrophages_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_year + Hypertension.composite +
GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) Age ORdate_year Hypertension.compositeyes GFR_MDRD
60.23181 0.01822 -0.03014 -0.17741 -0.01199
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7209 -0.6151 0.0356 0.5300 3.2322
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 40.726209 34.838142 1.169 0.24306
currentDF[, TRAIT] 0.006644 0.048108 0.138 0.89023
Age 0.017665 0.005835 3.027 0.00262 **
Gendermale 0.046571 0.104265 0.447 0.65535
ORdate_year -0.019704 0.017358 -1.135 0.25695
Hypertension.compositeyes -0.197858 0.125852 -1.572 0.11667
DiabetesStatusDiabetes 0.118305 0.116607 1.015 0.31090
SmokerStatusEx-smoker -0.008238 0.106515 -0.077 0.93839
SmokerStatusNever smoked -0.183500 0.161154 -1.139 0.25549
Med.Statin.LLDyes -0.092734 0.114736 -0.808 0.41941
Med.all.antiplateletyes 0.074854 0.152464 0.491 0.62371
GFR_MDRD -0.012440 0.002580 -4.821 2e-06 ***
BMI -0.007916 0.012602 -0.628 0.53026
MedHx_CVDyes 0.004123 0.098921 0.042 0.96677
stenose50-70% -1.402842 0.716674 -1.957 0.05096 .
stenose70-90% -1.233341 0.701613 -1.758 0.07950 .
stenose90-99% -1.090125 0.702139 -1.553 0.12128
stenose100% (Occlusion) -1.076516 1.205543 -0.893 0.37238
stenose50-99% -1.226178 0.908023 -1.350 0.17762
stenose70-99% -0.856478 0.860796 -0.995 0.32032
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9798 on 421 degrees of freedom
Multiple R-squared: 0.1334, Adjusted R-squared: 0.09427
F-statistic: 3.41 on 19 and 421 DF, p-value: 2.266e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' Macrophages_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: Macrophages_rank
Effect size...............: 0.006644
Standard error............: 0.048108
Odds ratio (effect size)..: 1.007
Lower 95% CI..............: 0.916
Upper 95% CI..............: 1.106
T-value...................: 0.138097
P-value...................: 0.8902299
R^2.......................: 0.133376
Adjusted r^2..............: 0.094265
Sample size of AE DB......: 2423
Sample size of model......: 441
Missing data %............: 81.79942
- processing SMC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_year + Hypertension.composite +
GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) Age ORdate_year Hypertension.compositeyes GFR_MDRD
63.88987 0.01814 -0.03195 -0.17375 -0.01218
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7295 -0.6076 0.0207 0.5262 3.2489
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 46.0163794 34.6165898 1.329 0.18447
currentDF[, TRAIT] 0.0006933 0.0536814 0.013 0.98970
Age 0.0173228 0.0058291 2.972 0.00313 **
Gendermale 0.0523617 0.1050134 0.499 0.61831
ORdate_year -0.0223218 0.0172461 -1.294 0.19627
Hypertension.compositeyes -0.1919688 0.1255314 -1.529 0.12696
DiabetesStatusDiabetes 0.1122621 0.1164524 0.964 0.33559
SmokerStatusEx-smoker 0.0007155 0.1058321 0.007 0.99461
SmokerStatusNever smoked -0.1796990 0.1610429 -1.116 0.26513
Med.Statin.LLDyes -0.1108490 0.1149391 -0.964 0.33539
Med.all.antiplateletyes 0.0822025 0.1521643 0.540 0.58933
GFR_MDRD -0.0126687 0.0025881 -4.895 1.4e-06 ***
BMI -0.0080347 0.0125716 -0.639 0.52309
MedHx_CVDyes 0.0114339 0.0987105 0.116 0.90784
stenose50-70% -1.3985133 0.7157309 -1.954 0.05137 .
stenose70-90% -1.2256052 0.7005735 -1.749 0.08095 .
stenose90-99% -1.0951519 0.7012203 -1.562 0.11909
stenose100% (Occlusion) -1.0897256 1.2029658 -0.906 0.36553
stenose50-99% -1.2017055 0.9062328 -1.326 0.18555
stenose70-99% -0.8568752 0.8599295 -0.996 0.31961
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9775 on 420 degrees of freedom
Multiple R-squared: 0.136, Adjusted R-squared: 0.09694
F-statistic: 3.48 on 19 and 420 DF, p-value: 1.475e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' SMC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: SMC_rank
Effect size...............: 0.000693
Standard error............: 0.053681
Odds ratio (effect size)..: 1.001
Lower 95% CI..............: 0.901
Upper 95% CI..............: 1.112
T-value...................: 0.012915
P-value...................: 0.9897017
R^2.......................: 0.136021
Adjusted r^2..............: 0.096936
Sample size of AE DB......: 2423
Sample size of model......: 440
Missing data %............: 81.84069
- processing VesselDensity_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + ORdate_year + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) Age ORdate_year GFR_MDRD
59.41440 0.01692 -0.02977 -0.01155
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7073 -0.6006 0.0184 0.5322 3.2760
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 58.0765945 36.4545769 1.593 0.1120
currentDF[, TRAIT] -0.0485472 0.0548620 -0.885 0.3768
Age 0.0161780 0.0063644 2.542 0.0114 *
Gendermale 0.0798771 0.1125868 0.709 0.4785
ORdate_year -0.0283604 0.0181657 -1.561 0.1193
Hypertension.compositeyes -0.1352392 0.1368671 -0.988 0.3237
DiabetesStatusDiabetes 0.1056803 0.1249207 0.846 0.3981
SmokerStatusEx-smoker -0.0349977 0.1143264 -0.306 0.7597
SmokerStatusNever smoked 0.0001821 0.1794264 0.001 0.9992
Med.Statin.LLDyes -0.0211208 0.1229850 -0.172 0.8637
Med.all.antiplateletyes 0.0386715 0.1676845 0.231 0.8177
GFR_MDRD -0.0119960 0.0027784 -4.318 2.02e-05 ***
BMI -0.0099595 0.0133094 -0.748 0.4547
MedHx_CVDyes 0.0436251 0.1075102 0.406 0.6851
stenose50-70% -1.2869830 0.7337236 -1.754 0.0802 .
stenose70-90% -1.1978619 0.7172468 -1.670 0.0957 .
stenose90-99% -1.0947221 0.7167510 -1.527 0.1275
stenose100% (Occlusion) -1.0801810 1.2250041 -0.882 0.3785
stenose50-99% -0.8072660 1.0070687 -0.802 0.4233
stenose70-99% -1.0889482 1.0034878 -1.085 0.2785
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9916 on 379 degrees of freedom
Multiple R-squared: 0.1175, Adjusted R-squared: 0.07327
F-statistic: 2.656 on 19 and 379 DF, p-value: 0.0002225
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' VesselDensity_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: VesselDensity_rank
Effect size...............: -0.048547
Standard error............: 0.054862
Odds ratio (effect size)..: 0.953
Lower 95% CI..............: 0.855
Upper 95% CI..............: 1.061
T-value...................: -0.884897
P-value...................: 0.3767735
R^2.......................: 0.117514
Adjusted r^2..............: 0.073274
Sample size of AE DB......: 2423
Sample size of model......: 399
Missing data %............: 83.53281 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Binary plaque traits
Analysis of binary plaque traits as a function of plasma/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(TRAITS.BIN)) {
TRAIT = TRAITS.BIN[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + ORdate_year + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age ORdate_year SmokerStatusEx-smoker
306.74558 -0.37332 0.02625 -0.15365 -0.42519
SmokerStatusNever smoked
-0.43321
Degrees of Freedom: 1025 Total (i.e. Null); 1020 Residual
Null Deviance: 1420
Residual Deviance: 1308 AIC: 1320
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8410 -1.0292 -0.6369 1.0688 2.0188
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 285.761462 48.999774 5.832 5.48e-09 ***
currentDF[, PROTEIN] -0.370483 0.070384 -5.264 1.41e-07 ***
Age 0.026493 0.008511 3.113 0.00185 **
Gendermale -0.098003 0.149705 -0.655 0.51270
ORdate_year -0.143421 0.024458 -5.864 4.52e-09 ***
Hypertension.compositeyes 0.257870 0.206176 1.251 0.21103
DiabetesStatusDiabetes -0.222991 0.164504 -1.356 0.17525
SmokerStatusEx-smoker -0.420947 0.155665 -2.704 0.00685 **
SmokerStatusNever smoked -0.480079 0.218238 -2.200 0.02782 *
Med.Statin.LLDyes -0.031126 0.165294 -0.188 0.85064
Med.all.antiplateletyes -0.065900 0.228570 -0.288 0.77311
GFR_MDRD 0.001002 0.003595 0.279 0.78053
BMI 0.021137 0.018738 1.128 0.25930
MedHx_CVDyes -0.036016 0.140041 -0.257 0.79704
stenose50-70% -0.626125 0.911908 -0.687 0.49233
stenose70-90% -0.196244 0.870308 -0.225 0.82160
stenose90-99% -0.146762 0.871650 -0.168 0.86629
stenose100% (Occlusion) 0.884665 1.202481 0.736 0.46191
stenose50-99% -13.973796 424.816726 -0.033 0.97376
stenose70-99% -0.254047 1.226249 -0.207 0.83587
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1420.3 on 1025 degrees of freedom
Residual deviance: 1295.2 on 1006 degrees of freedom
AIC: 1335.2
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.370483
Standard error............: 0.070384
Odds ratio (effect size)..: 0.69
Lower 95% CI..............: 0.601
Upper 95% CI..............: 0.793
Z-value...................: -5.263714
P-value...................: 1.411744e-07
Hosmer and Lemeshow r^2...: 0.088088
Cox and Snell r^2.........: 0.114798
Nagelkerke's pseudo r^2...: 0.153167
Sample size of AE DB......: 2423
Sample size of model......: 1026
Missing data %............: 57.6558
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
SmokerStatus + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] SmokerStatusEx-smoker SmokerStatusNever smoked BMI
0.48298 -0.23967 -0.39031 -0.63976 0.03909
MedHx_CVDyes
0.24930
Degrees of Freedom: 1026 Total (i.e. Null); 1021 Residual
Null Deviance: 1049
Residual Deviance: 1025 AIC: 1037
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2829 0.4536 0.6203 0.7202 1.1667
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.269e+00 9.654e+02 -0.003 0.99730
currentDF[, PROTEIN] -2.377e-01 8.148e-02 -2.917 0.00353 **
Age 1.331e-02 9.841e-03 1.352 0.17633
Gendermale -4.496e-02 1.763e-01 -0.255 0.79868
ORdate_year 8.537e-03 2.809e-02 0.304 0.76115
Hypertension.compositeyes 2.417e-01 2.285e-01 1.058 0.29009
DiabetesStatusDiabetes 7.633e-02 1.972e-01 0.387 0.69877
SmokerStatusEx-smoker -4.476e-01 1.892e-01 -2.365 0.01802 *
SmokerStatusNever smoked -7.654e-01 2.485e-01 -3.080 0.00207 **
Med.Statin.LLDyes -3.292e-05 1.934e-01 0.000 0.99986
Med.all.antiplateletyes 2.545e-01 2.597e-01 0.980 0.32706
GFR_MDRD 4.841e-03 4.242e-03 1.141 0.25380
BMI 4.198e-02 2.317e-02 1.812 0.06999 .
MedHx_CVDyes 2.202e-01 1.634e-01 1.348 0.17781
stenose50-70% -1.477e+01 9.638e+02 -0.015 0.98777
stenose70-90% -1.507e+01 9.638e+02 -0.016 0.98752
stenose90-99% -1.516e+01 9.638e+02 -0.016 0.98745
stenose100% (Occlusion) 1.433e-01 1.243e+03 0.000 0.99991
stenose50-99% -5.794e-02 1.515e+03 0.000 0.99997
stenose70-99% -1.467e+01 9.638e+02 -0.015 0.98786
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1048.5 on 1026 degrees of freedom
Residual deviance: 1010.9 on 1007 degrees of freedom
AIC: 1050.9
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.237708
Standard error............: 0.081478
Odds ratio (effect size)..: 0.788
Lower 95% CI..............: 0.672
Upper 95% CI..............: 0.925
Z-value...................: -2.917451
P-value...................: 0.00352905
Hosmer and Lemeshow r^2...: 0.035933
Cox and Snell r^2.........: 0.036022
Nagelkerke's pseudo r^2...: 0.056306
Sample size of AE DB......: 2423
Sample size of model......: 1027
Missing data %............: 57.61453
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
431.02422 0.37489 0.01595 0.98189 -0.21506
SmokerStatusEx-smoker SmokerStatusNever smoked
-0.29989 0.28776
Degrees of Freedom: 1026 Total (i.e. Null); 1020 Residual
Null Deviance: 1209
Residual Deviance: 1100 AIC: 1114
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6507 -0.9890 0.6020 0.8043 1.5467
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 447.833664 352.704584 1.270 0.2042
currentDF[, PROTEIN] 0.372607 0.083882 4.442 8.91e-06 ***
Age 0.018765 0.009275 2.023 0.0431 *
Gendermale 0.986859 0.162361 6.078 1.22e-09 ***
ORdate_year -0.216877 0.029241 -7.417 1.20e-13 ***
Hypertension.compositeyes -0.033938 0.229104 -0.148 0.8822
DiabetesStatusDiabetes -0.179848 0.180820 -0.995 0.3199
SmokerStatusEx-smoker -0.315938 0.174024 -1.815 0.0694 .
SmokerStatusNever smoked 0.273963 0.254420 1.077 0.2816
Med.Statin.LLDyes -0.051203 0.190672 -0.269 0.7883
Med.all.antiplateletyes 0.131593 0.256322 0.513 0.6077
GFR_MDRD 0.002480 0.003970 0.625 0.5321
BMI 0.004139 0.020330 0.204 0.8387
MedHx_CVDyes 0.089346 0.156373 0.571 0.5678
stenose50-70% -13.581609 347.797383 -0.039 0.9689
stenose70-90% -13.662819 347.797274 -0.039 0.9687
stenose90-99% -13.742492 347.797282 -0.040 0.9685
stenose100% (Occlusion) -14.314953 347.798015 -0.041 0.9672
stenose50-99% -15.225799 347.799293 -0.044 0.9651
stenose70-99% -14.062234 347.798217 -0.040 0.9677
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1209.2 on 1026 degrees of freedom
Residual deviance: 1090.5 on 1007 degrees of freedom
AIC: 1130.5
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.372607
Standard error............: 0.083882
Odds ratio (effect size)..: 1.452
Lower 95% CI..............: 1.231
Upper 95% CI..............: 1.711
Z-value...................: 4.442016
P-value...................: 8.912011e-06
Hosmer and Lemeshow r^2...: 0.09818
Cox and Snell r^2.........: 0.109168
Nagelkerke's pseudo r^2...: 0.157774
Sample size of AE DB......: 2423
Sample size of model......: 1027
Missing data %............: 57.61453
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_year +
BMI + MedHx_CVD, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year BMI MedHx_CVDyes
343.14552 0.55327 -0.17143 0.02864 0.38242
Degrees of Freedom: 1024 Total (i.e. Null); 1020 Residual
Null Deviance: 1371
Residual Deviance: 1287 AIC: 1297
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0000 -1.1648 0.7031 0.9550 1.7486
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 363.770975 50.776676 7.164 7.83e-13 ***
currentDF[, PROTEIN] 0.003007 0.071599 0.042 0.9665
Age 0.010212 0.008480 1.204 0.2285
Gendermale 0.600478 0.150064 4.001 6.29e-05 ***
ORdate_year -0.181776 0.025342 -7.173 7.34e-13 ***
Hypertension.compositeyes -0.132367 0.206940 -0.640 0.5224
DiabetesStatusDiabetes -0.118790 0.165012 -0.720 0.4716
SmokerStatusEx-smoker -0.102583 0.158189 -0.648 0.5167
SmokerStatusNever smoked -0.132896 0.217207 -0.612 0.5406
Med.Statin.LLDyes -0.116263 0.169843 -0.685 0.4936
Med.all.antiplateletyes 0.113072 0.231759 0.488 0.6256
GFR_MDRD -0.002828 0.003606 -0.784 0.4329
BMI 0.035890 0.019037 1.885 0.0594 .
MedHx_CVDyes 0.361928 0.140771 2.571 0.0101 *
stenose50-70% -0.445950 0.949101 -0.470 0.6385
stenose70-90% -0.406046 0.915562 -0.443 0.6574
stenose90-99% -0.340968 0.917561 -0.372 0.7102
stenose100% (Occlusion) -0.799853 1.142076 -0.700 0.4837
stenose50-99% 0.021625 1.361902 0.016 0.9873
stenose70-99% 1.876503 1.426514 1.315 0.1884
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1371.2 on 1024 degrees of freedom
Residual deviance: 1274.3 on 1005 degrees of freedom
AIC: 1314.3
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.003007
Standard error............: 0.071599
Odds ratio (effect size)..: 1.003
Lower 95% CI..............: 0.872
Upper 95% CI..............: 1.154
Z-value...................: 0.041998
P-value...................: 0.9665005
Hosmer and Lemeshow r^2...: 0.070627
Cox and Snell r^2.........: 0.090153
Nagelkerke's pseudo r^2...: 0.122232
Sample size of AE DB......: 2423
Sample size of model......: 1025
Missing data %............: 57.69707
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year SmokerStatusEx-smoker
239.70786 0.12206 0.56160 -0.11966 0.05632
SmokerStatusNever smoked Med.Statin.LLDyes Med.all.antiplateletyes
0.42368 0.37978 -0.32745
Degrees of Freedom: 1022 Total (i.e. Null); 1015 Residual
Null Deviance: 1417
Residual Deviance: 1366 AIC: 1382
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7922 -1.1428 0.7807 1.1043 1.6808
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 258.902238 48.251735 5.366 8.07e-08 ***
currentDF[, PROTEIN] 0.117462 0.067996 1.727 0.0841 .
Age -0.008074 0.008145 -0.991 0.3216
Gendermale 0.567843 0.145711 3.897 9.74e-05 ***
ORdate_year -0.128380 0.024068 -5.334 9.60e-08 ***
Hypertension.compositeyes -0.027769 0.198030 -0.140 0.8885
DiabetesStatusDiabetes -0.032774 0.158818 -0.206 0.8365
SmokerStatusEx-smoker 0.105992 0.150098 0.706 0.4801
SmokerStatusNever smoked 0.504479 0.213313 2.365 0.0180 *
Med.Statin.LLDyes 0.359217 0.160808 2.234 0.0255 *
Med.all.antiplateletyes -0.402587 0.226662 -1.776 0.0757 .
GFR_MDRD 0.001202 0.003453 0.348 0.7277
BMI -0.016533 0.018172 -0.910 0.3629
MedHx_CVDyes 0.145249 0.136130 1.067 0.2860
stenose50-70% -0.757805 0.925418 -0.819 0.4129
stenose70-90% -0.763753 0.891861 -0.856 0.3918
stenose90-99% -0.855585 0.893276 -0.958 0.3382
stenose100% (Occlusion) -1.804679 1.152101 -1.566 0.1172
stenose50-99% -0.255318 1.356697 -0.188 0.8507
stenose70-99% 0.242196 1.186145 0.204 0.8382
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1417.5 on 1022 degrees of freedom
Residual deviance: 1357.5 on 1003 degrees of freedom
AIC: 1397.5
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.117462
Standard error............: 0.067996
Odds ratio (effect size)..: 1.125
Lower 95% CI..............: 0.984
Upper 95% CI..............: 1.285
Z-value...................: 1.727479
P-value...................: 0.08408169
Hosmer and Lemeshow r^2...: 0.042305
Cox and Snell r^2.........: 0.056932
Nagelkerke's pseudo r^2...: 0.075927
Sample size of AE DB......: 2423
Sample size of model......: 1023
Missing data %............: 57.77961
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale SmokerStatusEx-smoker
3.02431 -0.17162 -0.02586 -0.44826 -0.05776
SmokerStatusNever smoked
-0.42741
Degrees of Freedom: 1022 Total (i.e. Null); 1017 Residual
Null Deviance: 1260
Residual Deviance: 1227 AIC: 1239
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0356 -1.3237 0.7327 0.8862 1.3272
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 20.199690 50.633230 0.399 0.68994
currentDF[, PROTEIN] -0.154953 0.073035 -2.122 0.03387 *
Age -0.022639 0.008956 -2.528 0.01147 *
Gendermale -0.477402 0.162885 -2.931 0.00338 **
ORdate_year -0.009191 0.025271 -0.364 0.71610
Hypertension.compositeyes 0.176298 0.211843 0.832 0.40529
DiabetesStatusDiabetes 0.013553 0.170661 0.079 0.93670
SmokerStatusEx-smoker -0.022550 0.164211 -0.137 0.89078
SmokerStatusNever smoked -0.413387 0.220990 -1.871 0.06140 .
Med.Statin.LLDyes 0.038173 0.171270 0.223 0.82363
Med.all.antiplateletyes -0.133109 0.237139 -0.561 0.57458
GFR_MDRD 0.005010 0.003739 1.340 0.18021
BMI -0.001614 0.019939 -0.081 0.93550
MedHx_CVDyes -0.051661 0.147390 -0.351 0.72596
stenose50-70% 0.367072 0.878777 0.418 0.67616
stenose70-90% 0.584415 0.841956 0.694 0.48761
stenose90-99% 0.895610 0.844117 1.061 0.28869
stenose100% (Occlusion) 0.522236 1.104289 0.473 0.63627
stenose50-99% 14.483400 427.080741 0.034 0.97295
stenose70-99% 0.443068 1.155843 0.383 0.70148
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1260.0 on 1022 degrees of freedom
Residual deviance: 1213.9 on 1003 degrees of freedom
AIC: 1253.9
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.154953
Standard error............: 0.073035
Odds ratio (effect size)..: 0.856
Lower 95% CI..............: 0.742
Upper 95% CI..............: 0.988
Z-value...................: -2.121631
P-value...................: 0.03386871
Hosmer and Lemeshow r^2...: 0.036611
Cox and Snell r^2.........: 0.044091
Nagelkerke's pseudo r^2...: 0.062259
Sample size of AE DB......: 2423
Sample size of model......: 1023
Missing data %............: 57.77961
Analysis of MCP1_pg_ml_2015_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + ORdate_year + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age ORdate_year SmokerStatusEx-smoker
279.63668 -0.39476 0.02869 -0.14023 -0.41699
SmokerStatusNever smoked
-0.46274
Degrees of Freedom: 1025 Total (i.e. Null); 1020 Residual
Null Deviance: 1420
Residual Deviance: 1308 AIC: 1320
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.863 -1.045 -0.608 1.074 2.130
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 257.844061 50.214433 5.135 2.82e-07 ***
currentDF[, PROTEIN] -0.402700 0.075598 -5.327 9.99e-08 ***
Age 0.029437 0.008526 3.453 0.000555 ***
Gendermale -0.003628 0.150954 -0.024 0.980824
ORdate_year -0.129597 0.025058 -5.172 2.32e-07 ***
Hypertension.compositeyes 0.283761 0.205758 1.379 0.167863
DiabetesStatusDiabetes -0.231758 0.164388 -1.410 0.158591
SmokerStatusEx-smoker -0.428649 0.155564 -2.755 0.005861 **
SmokerStatusNever smoked -0.502689 0.218425 -2.301 0.021368 *
Med.Statin.LLDyes -0.024746 0.165034 -0.150 0.880809
Med.all.antiplateletyes -0.052996 0.228424 -0.232 0.816534
GFR_MDRD 0.001570 0.003596 0.437 0.662445
BMI 0.021001 0.018720 1.122 0.261931
MedHx_CVDyes -0.040669 0.140089 -0.290 0.771581
stenose50-70% -0.823178 0.929921 -0.885 0.376042
stenose70-90% -0.353208 0.888418 -0.398 0.690948
stenose90-99% -0.317638 0.889813 -0.357 0.721113
stenose100% (Occlusion) 0.802490 1.222297 0.657 0.511475
stenose50-99% -14.187677 432.155336 -0.033 0.973810
stenose70-99% -0.451708 1.232581 -0.366 0.714012
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1420.3 on 1025 degrees of freedom
Residual deviance: 1294.3 on 1006 degrees of freedom
AIC: 1334.3
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.4027
Standard error............: 0.075598
Odds ratio (effect size)..: 0.669
Lower 95% CI..............: 0.576
Upper 95% CI..............: 0.775
Z-value...................: -5.32687
P-value...................: 9.991985e-08
Hosmer and Lemeshow r^2...: 0.088671
Cox and Snell r^2.........: 0.115512
Nagelkerke's pseudo r^2...: 0.154119
Sample size of AE DB......: 2423
Sample size of model......: 1026
Missing data %............: 57.6558
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year + SmokerStatus + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked
-82.29156 -0.31120 0.04123 -0.38704 -0.66237
BMI MedHx_CVDyes
0.04011 0.24788
Degrees of Freedom: 1026 Total (i.e. Null); 1020 Residual
Null Deviance: 1049
Residual Deviance: 1021 AIC: 1035
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3321 0.4334 0.6127 0.7234 1.1601
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.689e+01 9.545e+02 -0.039 0.969169
currentDF[, PROTEIN] -3.104e-01 8.728e-02 -3.556 0.000377 ***
Age 1.523e-02 9.880e-03 1.541 0.123267
Gendermale 3.495e-02 1.787e-01 0.196 0.844926
ORdate_year 2.524e-02 2.928e-02 0.862 0.388654
Hypertension.compositeyes 2.493e-01 2.289e-01 1.089 0.276119
DiabetesStatusDiabetes 7.073e-02 1.979e-01 0.358 0.720716
SmokerStatusEx-smoker -4.601e-01 1.902e-01 -2.419 0.015552 *
SmokerStatusNever smoked -7.831e-01 2.492e-01 -3.142 0.001676 **
Med.Statin.LLDyes -8.771e-04 1.935e-01 -0.005 0.996383
Med.all.antiplateletyes 2.678e-01 2.604e-01 1.029 0.303619
GFR_MDRD 5.127e-03 4.253e-03 1.206 0.227994
BMI 4.255e-02 2.333e-02 1.824 0.068186 .
MedHx_CVDyes 2.191e-01 1.637e-01 1.339 0.180703
stenose50-70% -1.490e+01 9.527e+02 -0.016 0.987519
stenose70-90% -1.519e+01 9.527e+02 -0.016 0.987282
stenose90-99% -1.529e+01 9.527e+02 -0.016 0.987198
stenose100% (Occlusion) 3.121e-02 1.235e+03 0.000 0.999980
stenose50-99% -2.727e-01 1.512e+03 0.000 0.999856
stenose70-99% -1.484e+01 9.527e+02 -0.016 0.987573
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1048.5 on 1026 degrees of freedom
Residual deviance: 1006.6 on 1007 degrees of freedom
AIC: 1046.6
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.310382
Standard error............: 0.087283
Odds ratio (effect size)..: 0.733
Lower 95% CI..............: 0.618
Upper 95% CI..............: 0.87
Z-value...................: -3.55603
P-value...................: 0.0003765011
Hosmer and Lemeshow r^2...: 0.040027
Cox and Snell r^2.........: 0.040043
Nagelkerke's pseudo r^2...: 0.06259
Sample size of AE DB......: 2423
Sample size of model......: 1027
Missing data %............: 57.61453
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
469.37418 0.45540 0.01347 0.86049 -0.23404
SmokerStatusEx-smoker SmokerStatusNever smoked
-0.29641 0.29609
Degrees of Freedom: 1026 Total (i.e. Null); 1020 Residual
Null Deviance: 1209
Residual Deviance: 1092 AIC: 1106
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.6326 -0.9680 0.5857 0.7829 1.6994
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 484.672877 355.498772 1.363 0.1728
currentDF[, PROTEIN] 0.447618 0.086377 5.182 2.19e-07 ***
Age 0.015842 0.009283 1.707 0.0879 .
Gendermale 0.870259 0.163696 5.316 1.06e-07 ***
ORdate_year -0.235162 0.030057 -7.824 5.12e-15 ***
Hypertension.compositeyes -0.053017 0.230251 -0.230 0.8179
DiabetesStatusDiabetes -0.183150 0.181689 -1.008 0.3134
SmokerStatusEx-smoker -0.316266 0.174529 -1.812 0.0700 .
SmokerStatusNever smoked 0.288237 0.255876 1.126 0.2600
Med.Statin.LLDyes -0.049076 0.191425 -0.256 0.7977
Med.all.antiplateletyes 0.096033 0.259188 0.371 0.7110
GFR_MDRD 0.001989 0.003985 0.499 0.6176
BMI 0.005657 0.020516 0.276 0.7828
MedHx_CVDyes 0.093233 0.157184 0.593 0.5531
stenose50-70% -13.345325 350.353303 -0.038 0.9696
stenose70-90% -13.480479 350.353191 -0.038 0.9693
stenose90-99% -13.551667 350.353198 -0.039 0.9691
stenose100% (Occlusion) -14.180536 350.353926 -0.040 0.9677
stenose50-99% -14.930710 350.355179 -0.043 0.9660
stenose70-99% -13.822004 350.354155 -0.039 0.9685
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1209.2 on 1026 degrees of freedom
Residual deviance: 1082.9 on 1007 degrees of freedom
AIC: 1122.9
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.447618
Standard error............: 0.086377
Odds ratio (effect size)..: 1.565
Lower 95% CI..............: 1.321
Upper 95% CI..............: 1.853
Z-value...................: 5.182132
P-value...................: 2.193639e-07
Hosmer and Lemeshow r^2...: 0.104428
Cox and Snell r^2.........: 0.115698
Nagelkerke's pseudo r^2...: 0.167211
Sample size of AE DB......: 2423
Sample size of model......: 1027
Missing data %............: 57.61453
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year BMI MedHx_CVDyes
392.07230 0.18061 0.50160 -0.19581 0.02969 0.39131
Degrees of Freedom: 1024 Total (i.e. Null); 1019 Residual
Null Deviance: 1371
Residual Deviance: 1281 AIC: 1293
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.1701 -1.1386 0.6982 0.9617 1.7551
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 413.430677 53.512385 7.726 1.11e-14 ***
currentDF[, PROTEIN] 0.180008 0.075716 2.377 0.017434 *
Age 0.010033 0.008479 1.183 0.236689
Gendermale 0.545667 0.152016 3.590 0.000331 ***
ORdate_year -0.206531 0.026700 -7.735 1.03e-14 ***
Hypertension.compositeyes -0.112636 0.207286 -0.543 0.586867
DiabetesStatusDiabetes -0.113506 0.165535 -0.686 0.492907
SmokerStatusEx-smoker -0.097149 0.158542 -0.613 0.540032
SmokerStatusNever smoked -0.141723 0.218025 -0.650 0.515672
Med.Statin.LLDyes -0.086084 0.170254 -0.506 0.613122
Med.all.antiplateletyes 0.103732 0.232980 0.445 0.656148
GFR_MDRD -0.002822 0.003613 -0.781 0.434817
BMI 0.036544 0.019035 1.920 0.054876 .
MedHx_CVDyes 0.365201 0.141153 2.587 0.009674 **
stenose50-70% -0.401826 0.943263 -0.426 0.670111
stenose70-90% -0.391222 0.909306 -0.430 0.667020
stenose90-99% -0.317648 0.911397 -0.349 0.727443
stenose100% (Occlusion) -0.745875 1.137193 -0.656 0.511894
stenose50-99% 0.123165 1.360810 0.091 0.927883
stenose70-99% 1.975403 1.425834 1.385 0.165919
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1371.2 on 1024 degrees of freedom
Residual deviance: 1268.6 on 1005 degrees of freedom
AIC: 1308.6
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IPH
Effect size...............: 0.180008
Standard error............: 0.075716
Odds ratio (effect size)..: 1.197
Lower 95% CI..............: 1.032
Upper 95% CI..............: 1.389
Z-value...................: 2.377427
P-value...................: 0.0174339
Hosmer and Lemeshow r^2...: 0.074795
Cox and Snell r^2.........: 0.095211
Nagelkerke's pseudo r^2...: 0.12909
Sample size of AE DB......: 2423
Sample size of model......: 1025
Missing data %............: 57.69707
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year SmokerStatusEx-smoker
275.73528 0.22784 0.50126 -0.13759 0.05857
SmokerStatusNever smoked Med.Statin.LLDyes Med.all.antiplateletyes
0.42075 0.40047 -0.33729
Degrees of Freedom: 1022 Total (i.e. Null); 1015 Residual
Null Deviance: 1417
Residual Deviance: 1359 AIC: 1375
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.903 -1.130 0.754 1.097 1.650
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 296.254795 50.487265 5.868 4.41e-09 ***
currentDF[, PROTEIN] 0.230423 0.072546 3.176 0.001492 **
Age -0.009277 0.008169 -1.136 0.256081
Gendermale 0.507404 0.147625 3.437 0.000588 ***
ORdate_year -0.146960 0.025176 -5.837 5.30e-09 ***
Hypertension.compositeyes -0.018654 0.198510 -0.094 0.925134
DiabetesStatusDiabetes -0.028077 0.159451 -0.176 0.860227
SmokerStatusEx-smoker 0.113143 0.150717 0.751 0.452835
SmokerStatusNever smoked 0.509672 0.214258 2.379 0.017370 *
Med.Statin.LLDyes 0.378901 0.161396 2.348 0.018892 *
Med.all.antiplateletyes -0.419125 0.227897 -1.839 0.065901 .
GFR_MDRD 0.001073 0.003467 0.310 0.756838
BMI -0.016377 0.018281 -0.896 0.370346
MedHx_CVDyes 0.147434 0.136619 1.079 0.280517
stenose50-70% -0.680770 0.923420 -0.737 0.460985
stenose70-90% -0.715597 0.889289 -0.805 0.421002
stenose90-99% -0.798488 0.890683 -0.896 0.369991
stenose100% (Occlusion) -1.742277 1.149497 -1.516 0.129599
stenose50-99% -0.131148 1.354973 -0.097 0.922893
stenose70-99% 0.356396 1.186652 0.300 0.763919
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1417.5 on 1022 degrees of freedom
Residual deviance: 1350.2 on 1003 degrees of freedom
AIC: 1390.2
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.230423
Standard error............: 0.072546
Odds ratio (effect size)..: 1.259
Lower 95% CI..............: 1.092
Upper 95% CI..............: 1.452
Z-value...................: 3.176236
P-value...................: 0.001491997
Hosmer and Lemeshow r^2...: 0.047428
Cox and Snell r^2.........: 0.063603
Nagelkerke's pseudo r^2...: 0.084824
Sample size of AE DB......: 2423
Sample size of model......: 1023
Missing data %............: 57.77961
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale SmokerStatusEx-smoker
2.86041 -0.28454 -0.02413 -0.37184 -0.06239
SmokerStatusNever smoked
-0.42455
Degrees of Freedom: 1022 Total (i.e. Null); 1017 Residual
Null Deviance: 1260
Residual Deviance: 1218 AIC: 1230
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0646 -1.2798 0.7265 0.8731 1.3692
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -21.133458 52.552076 -0.402 0.687579
currentDF[, PROTEIN] -0.284484 0.078214 -3.637 0.000276 ***
Age -0.021624 0.009007 -2.401 0.016362 *
Gendermale -0.400820 0.164887 -2.431 0.015062 *
ORdate_year 0.011380 0.026223 0.434 0.664310
Hypertension.compositeyes 0.176586 0.212257 0.832 0.405441
DiabetesStatusDiabetes 0.007316 0.171510 0.043 0.965977
SmokerStatusEx-smoker -0.030192 0.165026 -0.183 0.854833
SmokerStatusNever smoked -0.416898 0.222200 -1.876 0.060624 .
Med.Statin.LLDyes 0.020226 0.171804 0.118 0.906284
Med.all.antiplateletyes -0.121723 0.238580 -0.510 0.609914
GFR_MDRD 0.005240 0.003756 1.395 0.163024
BMI -0.002086 0.020132 -0.104 0.917473
MedHx_CVDyes -0.055130 0.148005 -0.372 0.709529
stenose50-70% 0.281200 0.883275 0.318 0.750211
stenose70-90% 0.529893 0.845581 0.627 0.530881
stenose90-99% 0.833372 0.847648 0.983 0.325530
stenose100% (Occlusion) 0.459584 1.110889 0.414 0.679088
stenose50-99% 14.334778 428.022224 0.033 0.973283
stenose70-99% 0.328315 1.165197 0.282 0.778122
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1260.0 on 1022 degrees of freedom
Residual deviance: 1204.9 on 1003 degrees of freedom
AIC: 1244.9
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.284484
Standard error............: 0.078214
Odds ratio (effect size)..: 0.752
Lower 95% CI..............: 0.645
Upper 95% CI..............: 0.877
Z-value...................: -3.637273
P-value...................: 0.0002755402
Hosmer and Lemeshow r^2...: 0.043715
Cox and Snell r^2.........: 0.052418
Nagelkerke's pseudo r^2...: 0.074016
Sample size of AE DB......: 2423
Sample size of model......: 1023
Missing data %............: 57.77961
Analysis of MCP1_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ ORdate_year + DiabetesStatus +
GFR_MDRD + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) ORdate_year DiabetesStatusDiabetes GFR_MDRD MedHx_CVDyes
-5.235e+02 2.619e-01 -4.535e-01 -9.264e-03 -3.696e-01
Degrees of Freedom: 497 Total (i.e. Null); 493 Residual
Null Deviance: 675.4
Residual Deviance: 656.8 AIC: 666.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7543 -1.2079 0.8134 1.0099 1.6284
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.303e+02 1.846e+02 -2.872 0.00408 **
currentDF[, PROTEIN] -9.703e-02 9.764e-02 -0.994 0.32037
Age 1.444e-03 1.244e-02 0.116 0.90761
Gendermale -6.259e-02 2.178e-01 -0.287 0.77380
ORdate_year 2.642e-01 9.214e-02 2.868 0.00414 **
Hypertension.compositeyes 3.948e-01 2.795e-01 1.413 0.15772
DiabetesStatusDiabetes -5.263e-01 2.397e-01 -2.196 0.02808 *
SmokerStatusEx-smoker -1.944e-01 2.133e-01 -0.911 0.36216
SmokerStatusNever smoked -9.473e-02 3.207e-01 -0.295 0.76768
Med.Statin.LLDyes -1.919e-01 2.227e-01 -0.861 0.38897
Med.all.antiplateletyes 2.845e-01 3.414e-01 0.833 0.40477
GFR_MDRD -9.088e-03 5.390e-03 -1.686 0.09180 .
BMI 1.066e-02 2.573e-02 0.414 0.67859
MedHx_CVDyes -3.417e-01 2.018e-01 -1.693 0.09049 .
stenose50-70% 1.416e+00 1.348e+00 1.050 0.29354
stenose70-90% 1.678e+00 1.259e+00 1.333 0.18247
stenose90-99% 1.412e+00 1.254e+00 1.126 0.26026
stenose100% (Occlusion) 1.621e+00 1.604e+00 1.011 0.31222
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 675.45 on 497 degrees of freedom
Residual deviance: 648.38 on 480 degrees of freedom
AIC: 684.38
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.097028
Standard error............: 0.097644
Odds ratio (effect size)..: 0.908
Lower 95% CI..............: 0.749
Upper 95% CI..............: 1.099
Z-value...................: -0.993689
P-value...................: 0.3203742
Hosmer and Lemeshow r^2...: 0.04007
Cox and Snell r^2.........: 0.052897
Nagelkerke's pseudo r^2...: 0.071252
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year + SmokerStatus + Med.all.antiplatelet, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked
-800.8702 -0.5054 0.4003 -0.5831 -0.9310
Med.all.antiplateletyes
0.7596
Degrees of Freedom: 495 Total (i.e. Null); 490 Residual
Null Deviance: 493.1
Residual Deviance: 447.1 AIC: 459.1
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3991 0.2993 0.4921 0.6733 1.3455
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.590e+02 8.436e+02 -0.900 0.368269
currentDF[, PROTEIN] -4.958e-01 1.301e-01 -3.812 0.000138 ***
Age -5.619e-03 1.617e-02 -0.347 0.728273
Gendermale -1.393e-01 2.861e-01 -0.487 0.626335
ORdate_year 3.866e-01 1.171e-01 3.302 0.000961 ***
Hypertension.compositeyes 2.724e-01 3.500e-01 0.778 0.436329
DiabetesStatusDiabetes 1.981e-01 3.234e-01 0.612 0.540222
SmokerStatusEx-smoker -5.865e-01 2.850e-01 -2.058 0.039624 *
SmokerStatusNever smoked -9.821e-01 3.912e-01 -2.510 0.012068 *
Med.Statin.LLDyes -9.019e-02 2.759e-01 -0.327 0.743778
Med.all.antiplateletyes 8.581e-01 4.047e-01 2.120 0.033973 *
GFR_MDRD -2.424e-03 7.057e-03 -0.344 0.731196
BMI -9.001e-03 3.494e-02 -0.258 0.796738
MedHx_CVDyes 7.111e-03 2.589e-01 0.027 0.978092
stenose50-70% -1.253e+01 8.103e+02 -0.015 0.987662
stenose70-90% -1.352e+01 8.103e+02 -0.017 0.986683
stenose90-99% -1.400e+01 8.103e+02 -0.017 0.986217
stenose100% (Occlusion) -1.323e+01 8.103e+02 -0.016 0.986975
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 493.05 on 495 degrees of freedom
Residual deviance: 439.04 on 478 degrees of freedom
AIC: 475.04
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: -0.495808
Standard error............: 0.130055
Odds ratio (effect size)..: 0.609
Lower 95% CI..............: 0.472
Upper 95% CI..............: 0.786
Z-value...................: -3.812285
P-value...................: 0.0001376877
Hosmer and Lemeshow r^2...: 0.10955
Cox and Snell r^2.........: 0.103179
Nagelkerke's pseudo r^2...: 0.163795
Sample size of AE DB......: 2423
Sample size of model......: 496
Missing data %............: 79.52951
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + Hypertension.composite + SmokerStatus, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale Hypertension.compositeyes SmokerStatusEx-smoker
0.8092 0.6602 0.6928 0.6592 -0.6083
SmokerStatusNever smoked
0.1413
Degrees of Freedom: 497 Total (i.e. Null); 492 Residual
Null Deviance: 491.1
Residual Deviance: 444.9 AIC: 456.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5848 0.3017 0.4903 0.6709 1.8103
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -3.019e+02 8.644e+02 -0.349 0.7269
currentDF[, PROTEIN] 6.900e-01 1.329e-01 5.190 2.11e-07 ***
Age 2.784e-03 1.595e-02 0.175 0.8614
Gendermale 6.538e-01 2.651e-01 2.467 0.0136 *
ORdate_year 1.572e-01 1.183e-01 1.328 0.1841
Hypertension.compositeyes 6.489e-01 3.426e-01 1.894 0.0582 .
DiabetesStatusDiabetes -3.041e-01 2.996e-01 -1.015 0.3102
SmokerStatusEx-smoker -6.573e-01 2.800e-01 -2.347 0.0189 *
SmokerStatusNever smoked 4.369e-02 4.552e-01 0.096 0.9235
Med.Statin.LLDyes -2.220e-01 2.967e-01 -0.748 0.4545
Med.all.antiplateletyes 2.654e-01 4.143e-01 0.641 0.5218
GFR_MDRD 1.690e-03 7.138e-03 0.237 0.8128
BMI 3.527e-02 3.295e-02 1.070 0.2844
MedHx_CVDyes 1.223e-01 2.549e-01 0.480 0.6313
stenose50-70% -1.438e+01 8.312e+02 -0.017 0.9862
stenose70-90% -1.327e+01 8.312e+02 -0.016 0.9873
stenose90-99% -1.361e+01 8.312e+02 -0.016 0.9869
stenose100% (Occlusion) -1.294e+01 8.312e+02 -0.016 0.9876
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 491.11 on 497 degrees of freedom
Residual deviance: 435.50 on 480 degrees of freedom
AIC: 471.5
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Fat10Perc
Effect size...............: 0.689959
Standard error............: 0.132948
Odds ratio (effect size)..: 1.994
Lower 95% CI..............: 1.536
Upper 95% CI..............: 2.587
Z-value...................: 5.1897
P-value...................: 2.106334e-07
Hosmer and Lemeshow r^2...: 0.113222
Cox and Snell r^2.........: 0.105647
Nagelkerke's pseudo r^2...: 0.168498
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
DiabetesStatus + BMI + MedHx_CVD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age Gendermale DiabetesStatusDiabetes BMI MedHx_CVDyes
-1.99101 0.01776 0.74177 -0.50330 0.05039 0.34743
Degrees of Freedom: 497 Total (i.e. Null); 492 Residual
Null Deviance: 552.3
Residual Deviance: 530.6 AIC: 542.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0930 0.4579 0.6185 0.7655 1.4577
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -44.157516 210.353643 -0.210 0.83373
currentDF[, PROTEIN] 0.085258 0.112450 0.758 0.44834
Age 0.013062 0.014168 0.922 0.35654
Gendermale 0.764000 0.235270 3.247 0.00116 **
ORdate_year 0.020756 0.104972 0.198 0.84326
Hypertension.compositeyes 0.238795 0.310574 0.769 0.44196
DiabetesStatusDiabetes -0.517784 0.264433 -1.958 0.05022 .
SmokerStatusEx-smoker -0.079166 0.246460 -0.321 0.74805
SmokerStatusNever smoked 0.053141 0.367384 0.145 0.88499
Med.Statin.LLDyes -0.087080 0.260840 -0.334 0.73850
Med.all.antiplateletyes -0.106642 0.399317 -0.267 0.78942
GFR_MDRD -0.005579 0.006259 -0.891 0.37278
BMI 0.050000 0.029351 1.704 0.08847 .
MedHx_CVDyes 0.344835 0.224565 1.536 0.12464
stenose50-70% 1.271129 1.377715 0.923 0.35620
stenose70-90% 1.170764 1.265627 0.925 0.35494
stenose90-99% 1.367189 1.262889 1.083 0.27899
stenose100% (Occlusion) 1.478726 1.723795 0.858 0.39099
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 552.26 on 497 degrees of freedom
Residual deviance: 526.41 on 480 degrees of freedom
AIC: 562.41
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IPH
Effect size...............: 0.085258
Standard error............: 0.11245
Odds ratio (effect size)..: 1.089
Lower 95% CI..............: 0.874
Upper 95% CI..............: 1.358
Z-value...................: 0.758181
P-value...................: 0.4483429
Hosmer and Lemeshow r^2...: 0.046812
Cox and Snell r^2.........: 0.050589
Nagelkerke's pseudo r^2...: 0.075495
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Gender + ORdate_year + Med.Statin.LLD + GFR_MDRD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Gendermale ORdate_year Med.Statin.LLDyes GFR_MDRD
-762.20937 0.39750 0.32386 0.38053 0.51246 -0.00836
Degrees of Freedom: 493 Total (i.e. Null); 488 Residual
Null Deviance: 671.2
Residual Deviance: 630.4 AIC: 642.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.0233 -1.1555 0.7355 0.9909 1.5585
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -7.459e+02 5.422e+02 -1.376 0.168912
currentDF[, PROTEIN] 3.810e-01 1.016e-01 3.748 0.000178 ***
Age -1.768e-02 1.269e-02 -1.393 0.163596
Gendermale 3.458e-01 2.195e-01 1.575 0.115240
ORdate_year 3.798e-01 9.574e-02 3.967 7.28e-05 ***
Hypertension.compositeyes 5.242e-02 2.887e-01 0.182 0.855941
DiabetesStatusDiabetes -1.422e-01 2.464e-01 -0.577 0.563990
SmokerStatusEx-smoker 5.878e-02 2.183e-01 0.269 0.787743
SmokerStatusNever smoked 1.942e-01 3.266e-01 0.595 0.552050
Med.Statin.LLDyes 4.288e-01 2.239e-01 1.915 0.055488 .
Med.all.antiplateletyes -1.192e-01 3.515e-01 -0.339 0.734579
GFR_MDRD -9.966e-03 5.527e-03 -1.803 0.071365 .
BMI -3.345e-03 2.565e-02 -0.130 0.896243
MedHx_CVDyes 1.279e-01 2.039e-01 0.627 0.530510
stenose50-70% -1.355e+01 5.071e+02 -0.027 0.978679
stenose70-90% -1.325e+01 5.071e+02 -0.026 0.979153
stenose90-99% -1.355e+01 5.071e+02 -0.027 0.978687
stenose100% (Occlusion) -1.393e+01 5.071e+02 -0.027 0.978091
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 671.15 on 493 degrees of freedom
Residual deviance: 623.39 on 476 degrees of freedom
AIC: 659.39
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MAC_binned
Effect size...............: 0.380999
Standard error............: 0.101645
Odds ratio (effect size)..: 1.464
Lower 95% CI..............: 1.199
Upper 95% CI..............: 1.786
Z-value...................: 3.748326
P-value...................: 0.0001780186
Hosmer and Lemeshow r^2...: 0.071171
Cox and Snell r^2.........: 0.092166
Nagelkerke's pseudo r^2...: 0.124048
Sample size of AE DB......: 2423
Sample size of model......: 494
Missing data %............: 79.61205
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale
3.58159 -0.46804 -0.03057 -0.73346
Degrees of Freedom: 495 Total (i.e. Null); 492 Residual
Null Deviance: 595.8
Residual Deviance: 558.3 AIC: 566.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3253 -1.1830 0.6182 0.8468 1.4395
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -2.655e+02 5.405e+02 -0.491 0.62331
currentDF[, PROTEIN] -4.611e-01 1.126e-01 -4.094 4.23e-05 ***
Age -3.691e-02 1.427e-02 -2.587 0.00969 **
Gendermale -8.251e-01 2.669e-01 -3.091 0.00199 **
ORdate_year 1.421e-01 1.019e-01 1.395 0.16311
Hypertension.compositeyes -3.209e-01 3.348e-01 -0.959 0.33780
DiabetesStatusDiabetes -2.069e-01 2.620e-01 -0.790 0.42963
SmokerStatusEx-smoker 2.049e-01 2.393e-01 0.856 0.39174
SmokerStatusNever smoked -1.393e-01 3.395e-01 -0.410 0.68158
Med.Statin.LLDyes -1.806e-01 2.464e-01 -0.733 0.46353
Med.all.antiplateletyes -8.168e-02 3.820e-01 -0.214 0.83070
GFR_MDRD -1.716e-03 5.923e-03 -0.290 0.77209
BMI -2.059e-02 2.927e-02 -0.703 0.48175
MedHx_CVDyes -1.088e-01 2.247e-01 -0.484 0.62815
stenose50-70% -1.382e+01 5.005e+02 -0.028 0.97797
stenose70-90% -1.404e+01 5.005e+02 -0.028 0.97762
stenose90-99% -1.391e+01 5.005e+02 -0.028 0.97782
stenose100% (Occlusion) -1.486e+01 5.005e+02 -0.030 0.97631
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 595.82 on 495 degrees of freedom
Residual deviance: 547.99 on 478 degrees of freedom
AIC: 583.99
Number of Fisher Scoring iterations: 13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.461087
Standard error............: 0.112615
Odds ratio (effect size)..: 0.631
Lower 95% CI..............: 0.506
Upper 95% CI..............: 0.786
Z-value...................: -4.094379
P-value...................: 4.233022e-05
Hosmer and Lemeshow r^2...: 0.080275
Cox and Snell r^2.........: 0.091928
Nagelkerke's pseudo r^2...: 0.131479
Sample size of AE DB......: 2423
Sample size of model......: 496
Missing data %............: 79.52951
Analysis of MCP1_plasma_olink_rank.
- processing CalcificationPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + ORdate_year +
Hypertension.composite + stenose, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age ORdate_year Hypertension.compositeyes stenose50-70%
242.46963 0.01868 -0.12931 0.38932 15.17511
stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
15.46546 15.03112 30.56881 0.51438 14.11539
stenose99
31.62050
Degrees of Freedom: 477 Total (i.e. Null); 467 Residual
Null Deviance: 652.9
Residual Deviance: 616.2 AIC: 638.2
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7050 -1.0387 -0.7367 1.1805 2.2693
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.426e+02 7.908e+02 0.307 0.7590
currentDF[, PROTEIN] -4.091e-03 1.011e-01 -0.040 0.9677
Age 2.108e-02 1.231e-02 1.712 0.0868 .
Gendermale -1.561e-01 2.144e-01 -0.728 0.4666
ORdate_year -1.294e-01 3.213e-02 -4.028 5.63e-05 ***
Hypertension.compositeyes 4.027e-01 2.691e-01 1.497 0.1345
DiabetesStatusDiabetes -7.124e-02 2.372e-01 -0.300 0.7640
SmokerStatusEx-smoker 1.162e-01 2.187e-01 0.532 0.5950
SmokerStatusNever smoked -3.183e-01 3.318e-01 -0.959 0.3375
Med.Statin.LLDyes -1.724e-01 2.362e-01 -0.730 0.4654
Med.all.antiplateletyes 2.056e-01 3.146e-01 0.654 0.5134
GFR_MDRD 2.562e-03 5.366e-03 0.477 0.6330
BMI -1.079e-02 2.601e-02 -0.415 0.6782
MedHx_CVDyes 2.675e-01 2.046e-01 1.307 0.1911
stenose50-70% 1.507e+01 7.881e+02 0.019 0.9847
stenose70-90% 1.539e+01 7.881e+02 0.020 0.9844
stenose90-99% 1.495e+01 7.881e+02 0.019 0.9849
stenose100% (Occlusion) 3.035e+01 1.655e+03 0.018 0.9854
stenose50-99% 3.859e-01 1.148e+03 0.000 0.9997
stenose70-99% 1.407e+01 7.881e+02 0.018 0.9858
stenose99 3.142e+01 1.655e+03 0.019 0.9849
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 652.94 on 477 degrees of freedom
Residual deviance: 610.97 on 457 degrees of freedom
AIC: 652.97
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' CalcificationPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: CalcificationPlaque
Effect size...............: -0.004091
Standard error............: 0.101059
Odds ratio (effect size)..: 0.996
Lower 95% CI..............: 0.817
Upper 95% CI..............: 1.214
Z-value...................: -0.040485
P-value...................: 0.9677061
Hosmer and Lemeshow r^2...: 0.064281
Cox and Snell r^2.........: 0.084063
Nagelkerke's pseudo r^2...: 0.112855
Sample size of AE DB......: 2423
Sample size of model......: 478
Missing data %............: 80.27239
- processing CollagenPlaque
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + SmokerStatus +
MedHx_CVD, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age SmokerStatusEx-smoker SmokerStatusNever smoked MedHx_CVDyes
-0.34941 0.02267 -0.49730 -0.80175 0.52239
Degrees of Freedom: 476 Total (i.e. Null); 472 Residual
Null Deviance: 526.9
Residual Deviance: 511.8 AIC: 521.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.26979 0.00059 0.64407 0.77941 1.13730
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.768e+01 8.141e+02 0.034 0.9729
currentDF[, PROTEIN] 3.621e-03 1.164e-01 0.031 0.9752
Age 2.692e-02 1.396e-02 1.929 0.0537 .
Gendermale -1.205e-01 2.451e-01 -0.491 0.6231
ORdate_year -7.347e-03 3.547e-02 -0.207 0.8359
Hypertension.compositeyes 4.535e-02 2.993e-01 0.152 0.8796
DiabetesStatusDiabetes -1.455e-01 2.708e-01 -0.537 0.5910
SmokerStatusEx-smoker -5.394e-01 2.587e-01 -2.085 0.0371 *
SmokerStatusNever smoked -8.649e-01 3.628e-01 -2.384 0.0171 *
Med.Statin.LLDyes 3.864e-02 2.674e-01 0.145 0.8851
Med.all.antiplateletyes 4.056e-01 3.384e-01 1.199 0.2306
GFR_MDRD 2.160e-04 6.212e-03 0.035 0.9723
BMI 2.500e-02 3.014e-02 0.829 0.4069
MedHx_CVDyes 5.091e-01 2.264e-01 2.248 0.0245 *
stenose50-70% -1.415e+01 8.110e+02 -0.017 0.9861
stenose70-90% -1.448e+01 8.110e+02 -0.018 0.9858
stenose90-99% -1.464e+01 8.110e+02 -0.018 0.9856
stenose100% (Occlusion) -3.527e-01 1.666e+03 0.000 0.9998
stenose50-99% -5.474e-01 1.156e+03 0.000 0.9996
stenose70-99% -1.471e+01 8.110e+02 -0.018 0.9855
stenose99 -9.358e-01 1.666e+03 -0.001 0.9996
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 526.93 on 476 degrees of freedom
Residual deviance: 503.73 on 456 degrees of freedom
AIC: 545.73
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' CollagenPlaque ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: CollagenPlaque
Effect size...............: 0.003621
Standard error............: 0.116419
Odds ratio (effect size)..: 1.004
Lower 95% CI..............: 0.799
Upper 95% CI..............: 1.261
Z-value...................: 0.0311
P-value...................: 0.9751895
Hosmer and Lemeshow r^2...: 0.044026
Cox and Snell r^2.........: 0.047471
Nagelkerke's pseudo r^2...: 0.070992
Sample size of AE DB......: 2423
Sample size of model......: 477
Missing data %............: 80.31366
- processing Fat10Perc
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age + Gender +
ORdate_year + SmokerStatus + Med.all.antiplatelet + BMI,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Age Gendermale ORdate_year SmokerStatusEx-smoker
116.16898 0.02032 1.02176 -0.05944 0.13859
SmokerStatusNever smoked Med.all.antiplateletyes BMI
0.76181 0.63953 0.04973
Degrees of Freedom: 477 Total (i.e. Null); 470 Residual
Null Deviance: 576.3
Residual Deviance: 537.4 AIC: 553.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.2463 -1.0501 0.6439 0.7951 1.4627
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.392e+02 8.423e+02 0.165 0.8687
currentDF[, PROTEIN] -1.551e-01 1.130e-01 -1.372 0.1700
Age 2.678e-02 1.348e-02 1.987 0.0470 *
Gendermale 1.040e+00 2.280e-01 4.561 5.08e-06 ***
ORdate_year -6.400e-02 3.476e-02 -1.841 0.0656 .
Hypertension.compositeyes -1.060e-01 2.929e-01 -0.362 0.7173
DiabetesStatusDiabetes 4.717e-02 2.671e-01 0.177 0.8598
SmokerStatusEx-smoker 1.024e-01 2.393e-01 0.428 0.6687
SmokerStatusNever smoked 7.014e-01 3.809e-01 1.841 0.0655 .
Med.Statin.LLDyes -1.387e-01 2.695e-01 -0.515 0.6068
Med.all.antiplateletyes 6.949e-01 3.260e-01 2.132 0.0330 *
GFR_MDRD 1.454e-03 5.981e-03 0.243 0.8079
BMI 4.699e-02 2.805e-02 1.675 0.0938 .
MedHx_CVDyes -6.523e-02 2.258e-01 -0.289 0.7727
stenose50-70% -1.403e+01 8.394e+02 -0.017 0.9867
stenose70-90% -1.413e+01 8.394e+02 -0.017 0.9866
stenose90-99% -1.422e+01 8.394e+02 -0.017 0.9865
stenose100% (Occlusion) -3.114e+01 1.680e+03 -0.019 0.9852
stenose50-99% -3.046e+01 1.166e+03 -0.026 0.9792
stenose70-99% -1.408e+01 8.394e+02 -0.017 0.9866
stenose99 2.649e-01 1.680e+03 0.000 0.9999
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 576.34 on 477 degrees of freedom
Residual deviance: 522.60 on 457 degrees of freedom
AIC: 564.6
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' Fat10Perc ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: Fat10Perc
Effect size...............: -0.155125
Standard error............: 0.113044
Odds ratio (effect size)..: 0.856
Lower 95% CI..............: 0.686
Upper 95% CI..............: 1.069
Z-value...................: -1.372251
P-value...................: 0.1699853
Hosmer and Lemeshow r^2...: 0.093229
Cox and Snell r^2.........: 0.106321
Nagelkerke's pseudo r^2...: 0.151773
Sample size of AE DB......: 2423
Sample size of model......: 478
Missing data %............: 80.27239
- processing IPH
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_year +
MedHx_CVD, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year MedHx_CVDyes
187.67641 0.62406 -0.09354 0.36848
Degrees of Freedom: 476 Total (i.e. Null); 473 Residual
Null Deviance: 634.2
Residual Deviance: 610.8 AIC: 618.8
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.9260 -1.2247 0.7484 0.9732 1.7286
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.270e+02 8.067e+02 0.281 0.77839
currentDF[, PROTEIN] 6.743e-02 1.033e-01 0.652 0.51413
Age -6.302e-03 1.243e-02 -0.507 0.61223
Gendermale 5.890e-01 2.140e-01 2.752 0.00591 **
ORdate_year -1.055e-01 3.225e-02 -3.271 0.00107 **
Hypertension.compositeyes 7.644e-02 2.684e-01 0.285 0.77581
DiabetesStatusDiabetes -5.900e-03 2.441e-01 -0.024 0.98072
SmokerStatusEx-smoker 8.226e-02 2.240e-01 0.367 0.71344
SmokerStatusNever smoked -1.627e-02 3.285e-01 -0.050 0.96049
Med.Statin.LLDyes -2.222e-01 2.443e-01 -0.910 0.36305
Med.all.antiplateletyes -3.344e-01 3.248e-01 -1.029 0.30331
GFR_MDRD 3.247e-03 5.487e-03 0.592 0.55402
BMI 1.205e-02 2.607e-02 0.462 0.64390
MedHx_CVDyes 3.709e-01 2.051e-01 1.808 0.07058 .
stenose50-70% -1.537e+01 8.041e+02 -0.019 0.98474
stenose70-90% -1.508e+01 8.041e+02 -0.019 0.98504
stenose90-99% -1.524e+01 8.041e+02 -0.019 0.98488
stenose100% (Occlusion) -5.827e-01 1.663e+03 0.000 0.99972
stenose50-99% 5.007e-01 1.159e+03 0.000 0.99966
stenose70-99% -1.417e+01 8.041e+02 -0.018 0.98594
stenose99 1.872e-01 1.663e+03 0.000 0.99991
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 634.24 on 476 degrees of freedom
Residual deviance: 596.32 on 456 degrees of freedom
AIC: 638.32
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IPH ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IPH
Effect size...............: 0.067426
Standard error............: 0.103347
Odds ratio (effect size)..: 1.07
Lower 95% CI..............: 0.874
Upper 95% CI..............: 1.31
Z-value...................: 0.652426
P-value...................: 0.5141265
Hosmer and Lemeshow r^2...: 0.059783
Cox and Snell r^2.........: 0.076413
Nagelkerke's pseudo r^2...: 0.103903
Sample size of AE DB......: 2423
Sample size of model......: 477
Missing data %............: 80.31366
- processing MAC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Gender + ORdate_year +
DiabetesStatus, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year DiabetesStatusDiabetes
144.80334 0.80350 -0.07238 0.32769
Degrees of Freedom: 472 Total (i.e. Null); 469 Residual
Null Deviance: 654.8
Residual Deviance: 630.7 AIC: 638.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.7665 -1.1491 0.7966 1.0834 1.6665
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.444e+02 6.321e+01 2.284 0.022350 *
currentDF[, PROTEIN] -4.429e-02 1.003e-01 -0.442 0.658726
Age -1.309e-03 1.222e-02 -0.107 0.914662
Gendermale 7.679e-01 2.136e-01 3.595 0.000324 ***
ORdate_year -7.168e-02 3.151e-02 -2.275 0.022920 *
Hypertension.compositeyes -1.515e-01 2.636e-01 -0.575 0.565492
DiabetesStatusDiabetes 3.645e-01 2.389e-01 1.525 0.127187
SmokerStatusEx-smoker 2.504e-01 2.184e-01 1.147 0.251489
SmokerStatusNever smoked 1.016e-01 3.240e-01 0.314 0.753846
Med.Statin.LLDyes 3.563e-02 2.359e-01 0.151 0.879943
Med.all.antiplateletyes 2.106e-01 3.120e-01 0.675 0.499721
GFR_MDRD -3.980e-03 5.346e-03 -0.745 0.456546
BMI -1.807e-02 2.552e-02 -0.708 0.478937
MedHx_CVDyes 1.037e-01 2.017e-01 0.514 0.607177
stenose50-70% -4.346e-01 1.284e+00 -0.339 0.734908
stenose70-90% -5.554e-01 1.253e+00 -0.443 0.657546
stenose90-99% -2.760e-01 1.256e+00 -0.220 0.826036
stenose100% (Occlusion) 1.445e+01 1.455e+03 0.010 0.992077
stenose50-99% -1.571e+01 8.078e+02 -0.019 0.984483
stenose70-99% 7.089e-01 1.513e+00 0.469 0.639394
stenose99 1.536e+01 1.455e+03 0.011 0.991578
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 654.78 on 472 degrees of freedom
Residual deviance: 617.31 on 452 degrees of freedom
AIC: 659.31
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MAC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MAC_binned
Effect size...............: -0.044293
Standard error............: 0.100284
Odds ratio (effect size)..: 0.957
Lower 95% CI..............: 0.786
Upper 95% CI..............: 1.164
Z-value...................: -0.441672
P-value...................: 0.6587264
Hosmer and Lemeshow r^2...: 0.057225
Cox and Snell r^2.........: 0.076161
Nagelkerke's pseudo r^2...: 0.101615
Sample size of AE DB......: 2423
Sample size of model......: 473
Missing data %............: 80.47875
- processing SMC_binned
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ Age, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) Age
2.36154 -0.02275
Degrees of Freedom: 473 Total (i.e. Null); 472 Residual
Null Deviance: 591.7
Residual Deviance: 587.4 AIC: 591.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-1.8310 -1.3745 0.7761 0.8955 1.4757
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 8.727e+01 6.662e+01 1.310 0.190
currentDF[, PROTEIN] -1.060e-01 1.052e-01 -1.008 0.314
Age -1.665e-02 1.300e-02 -1.281 0.200
Gendermale -2.728e-01 2.283e-01 -1.195 0.232
ORdate_year -4.264e-02 3.321e-02 -1.284 0.199
Hypertension.compositeyes -1.759e-02 2.803e-01 -0.063 0.950
DiabetesStatusDiabetes -1.400e-01 2.445e-01 -0.572 0.567
SmokerStatusEx-smoker -1.023e-02 2.317e-01 -0.044 0.965
SmokerStatusNever smoked -3.630e-01 3.365e-01 -1.079 0.281
Med.Statin.LLDyes -5.187e-02 2.478e-01 -0.209 0.834
Med.all.antiplateletyes -4.945e-02 3.258e-01 -0.152 0.879
GFR_MDRD -4.442e-03 5.599e-03 -0.793 0.428
BMI 1.545e-03 2.717e-02 0.057 0.955
MedHx_CVDyes -1.521e-01 2.142e-01 -0.710 0.478
stenose50-70% 1.028e+00 1.286e+00 0.799 0.424
stenose70-90% 1.004e+00 1.252e+00 0.802 0.422
stenose90-99% 1.027e+00 1.255e+00 0.819 0.413
stenose100% (Occlusion) 1.583e+01 1.455e+03 0.011 0.991
stenose50-99% 1.607e+01 8.348e+02 0.019 0.985
stenose70-99% 3.637e-01 1.466e+00 0.248 0.804
stenose99 1.681e+01 1.455e+03 0.012 0.991
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 591.71 on 473 degrees of freedom
Residual deviance: 574.06 on 453 degrees of freedom
AIC: 616.06
Number of Fisher Scoring iterations: 14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' SMC_binned ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: SMC_binned
Effect size...............: -0.106025
Standard error............: 0.105233
Odds ratio (effect size)..: 0.899
Lower 95% CI..............: 0.732
Upper 95% CI..............: 1.105
Z-value...................: -1.007523
P-value...................: 0.3136834
Hosmer and Lemeshow r^2...: 0.029825
Cox and Snell r^2.........: 0.036547
Nagelkerke's pseudo r^2...: 0.051257
Sample size of AE DB......: 2423
Sample size of model......: 474
Missing data %............: 80.43747 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
B. Cross-sectional analysis symptoms
We will perform a cross-sectional analysis between plaque and plasma MCP1, IL6, and IL6R levels and the ‘clinical status’ of the plaque in terms of presence of patients’ symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:
- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic
Model 1
In this model we correct for Age, Gender, and year of surgery.
NOTE Given that MCP1 plasma was only measured in symptomatic patients, we exclude it from this analysis.
TRAITS.PROTEIN.RANK.limit <- c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank", "MCP1_rank")
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK.limit)) {
PROTEIN = TRAITS.PROTEIN.RANK.limit[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
-154.08068 0.20536 0.03017 -0.44786 0.07699
Degrees of Freedom: 1197 Total (i.e. Null); 1193 Residual
Null Deviance: 827
Residual Deviance: 799.6 AIC: 809.6
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5054 0.3522 0.4394 0.5296 0.8341
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -154.08068 66.59026 -2.314 0.02068 *
currentDF[, PROTEIN] 0.20536 0.09573 2.145 0.03194 *
Age 0.03017 0.01025 2.945 0.00323 **
Gendermale -0.44786 0.21848 -2.050 0.04037 *
ORdate_year 0.07699 0.03322 2.318 0.02047 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 826.98 on 1197 degrees of freedom
Residual deviance: 799.57 on 1193 degrees of freedom
AIC: 809.57
Number of Fisher Scoring iterations: 5
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.205363
Standard error............: 0.095734
Odds ratio (effect size)..: 1.228
Lower 95% CI..............: 1.018
Upper 95% CI..............: 1.481
Z-value...................: 2.145142
P-value...................: 0.03194153
Hosmer and Lemeshow r^2...: 0.033147
Cox and Snell r^2.........: 0.022621
Nagelkerke's pseudo r^2...: 0.045372
Sample size of AE DB......: 2423
Sample size of model......: 1198
Missing data %............: 50.55716
Analysis of MCP1_pg_ml_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year
-124.83943 0.27031 0.02877 -0.51749 0.06249
Degrees of Freedom: 1198 Total (i.e. Null); 1194 Residual
Null Deviance: 827.2
Residual Deviance: 797.3 AIC: 807.3
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.5524 0.3495 0.4339 0.5243 0.8965
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -124.83943 68.66012 -1.818 0.06903 .
currentDF[, PROTEIN] 0.27031 0.10237 2.641 0.00828 **
Age 0.02877 0.01023 2.811 0.00493 **
Gendermale -0.51749 0.22116 -2.340 0.01929 *
ORdate_year 0.06249 0.03424 1.825 0.06796 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 827.22 on 1198 degrees of freedom
Residual deviance: 797.31 on 1194 degrees of freedom
AIC: 807.31
Number of Fisher Scoring iterations: 5
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.270307
Standard error............: 0.102367
Odds ratio (effect size)..: 1.31
Lower 95% CI..............: 1.072
Upper 95% CI..............: 1.602
Z-value...................: 2.640561
P-value...................: 0.008276887
Hosmer and Lemeshow r^2...: 0.036146
Cox and Snell r^2.........: 0.02463
Nagelkerke's pseudo r^2...: 0.049419
Sample size of AE DB......: 2423
Sample size of model......: 1199
Missing data %............: 50.51589
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year, family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year
-473.3895 0.3371 0.2371
Degrees of Freedom: 555 Total (i.e. Null); 553 Residual
Null Deviance: 479
Residual Deviance: 468.7 AIC: 474.7
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.3911 0.4414 0.5340 0.6219 1.0452
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -480.62215 225.42835 -2.132 0.03300 *
currentDF[, PROTEIN] 0.36235 0.12346 2.935 0.00334 **
Age 0.01562 0.01370 1.140 0.25440
Gendermale -0.29174 0.27407 -1.064 0.28711
ORdate_year 0.24030 0.11253 2.135 0.03272 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 478.98 on 555 degrees of freedom
Residual deviance: 466.39 on 551 degrees of freedom
AIC: 476.39
Number of Fisher Scoring iterations: 4
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.362354
Standard error............: 0.123463
Odds ratio (effect size)..: 1.437
Lower 95% CI..............: 1.128
Upper 95% CI..............: 1.83
Z-value...................: 2.934919
P-value...................: 0.003336347
Hosmer and Lemeshow r^2...: 0.026279
Cox and Snell r^2.........: 0.022385
Nagelkerke's pseudo r^2...: 0.038764
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..
NOTE Given that MCP1 plasma was only measured in symptomatic patients, we exclude it from this analysis.
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK.limit)) {
PROTEIN = TRAITS.PROTEIN.RANK.limit[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "AsymptSympt"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF, family = binomial(link = "logit"))
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
Analysis of MCP1_pg_ug_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Med.all.antiplatelet + stenose,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year Med.all.antiplateletyes
-184.26357 0.20674 0.02327 -0.40571 0.09989 -0.91474
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-13.17892 -14.73049 -14.37151 -0.01341 -15.95925 -0.80994
Degrees of Freedom: 1037 Total (i.e. Null); 1026 Residual
Null Deviance: 726.9
Residual Deviance: 684.5 AIC: 708.5
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.1947 0.2880 0.4269 0.5447 0.9485
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.877e+02 9.601e+02 -0.196 0.84498
currentDF[, PROTEIN] 1.901e-01 1.033e-01 1.841 0.06562 .
Age 3.089e-02 1.279e-02 2.415 0.01573 *
Gendermale -3.560e-01 2.377e-01 -1.498 0.13419
ORdate_year 1.016e-01 3.829e-02 2.653 0.00798 **
Hypertension.compositeyes -3.381e-01 3.453e-01 -0.979 0.32749
DiabetesStatusDiabetes -5.824e-02 2.435e-01 -0.239 0.81098
SmokerStatusEx-smoker -3.503e-01 2.342e-01 -1.496 0.13476
SmokerStatusNever smoked -2.595e-02 3.566e-01 -0.073 0.94200
Med.Statin.LLDyes -2.571e-01 2.685e-01 -0.958 0.33821
Med.all.antiplateletyes -9.250e-01 4.808e-01 -1.924 0.05440 .
GFR_MDRD 6.192e-03 5.521e-03 1.122 0.26200
BMI -9.105e-03 2.814e-02 -0.324 0.74627
MedHx_CVDyes 8.733e-02 2.104e-01 0.415 0.67811
stenose50-70% -1.326e+01 9.570e+02 -0.014 0.98895
stenose70-90% -1.478e+01 9.570e+02 -0.015 0.98768
stenose90-99% -1.443e+01 9.570e+02 -0.015 0.98797
stenose100% (Occlusion) -1.944e-01 1.236e+03 0.000 0.99987
stenose50-99% -1.625e+01 9.570e+02 -0.017 0.98645
stenose70-99% -8.673e-01 1.194e+03 -0.001 0.99942
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 726.94 on 1037 degrees of freedom
Residual deviance: 677.19 on 1018 degrees of freedom
AIC: 717.19
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.190126
Standard error............: 0.103273
Odds ratio (effect size)..: 1.209
Lower 95% CI..............: 0.988
Upper 95% CI..............: 1.481
Z-value...................: 1.840995
P-value...................: 0.06562231
Hosmer and Lemeshow r^2...: 0.06844
Cox and Snell r^2.........: 0.0468
Nagelkerke's pseudo r^2...: 0.092935
Sample size of AE DB......: 2423
Sample size of model......: 1038
Missing data %............: 57.16054
Analysis of MCP1_pg_ml_2015_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Med.all.antiplatelet + stenose,
family = binomial(link = "logit"), data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] Age Gendermale ORdate_year Med.all.antiplateletyes
-145.03274 0.32511 0.02147 -0.48726 0.08040 -0.91400
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion) stenose50-99% stenose70-99%
-13.07895 -14.66964 -14.29506 0.03649 -15.84146 -0.73998
Degrees of Freedom: 1037 Total (i.e. Null); 1026 Residual
Null Deviance: 726.9
Residual Deviance: 679.9 AIC: 703.9
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.2614 0.2818 0.4201 0.5408 1.0245
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.486e+02 9.514e+02 -0.156 0.87591
currentDF[, PROTEIN] 3.063e-01 1.122e-01 2.729 0.00636 **
Age 2.888e-02 1.278e-02 2.260 0.02385 *
Gendermale -4.336e-01 2.409e-01 -1.800 0.07187 .
ORdate_year 8.214e-02 3.946e-02 2.082 0.03739 *
Hypertension.compositeyes -3.362e-01 3.458e-01 -0.972 0.33083
DiabetesStatusDiabetes -4.766e-02 2.441e-01 -0.195 0.84521
SmokerStatusEx-smoker -3.345e-01 2.345e-01 -1.426 0.15373
SmokerStatusNever smoked -2.811e-03 3.574e-01 -0.008 0.99372
Med.Statin.LLDyes -2.461e-01 2.688e-01 -0.916 0.35983
Med.all.antiplateletyes -9.270e-01 4.806e-01 -1.929 0.05372 .
GFR_MDRD 6.238e-03 5.532e-03 1.128 0.25950
BMI -8.706e-03 2.805e-02 -0.310 0.75628
MedHx_CVDyes 9.157e-02 2.110e-01 0.434 0.66436
stenose50-70% -1.317e+01 9.481e+02 -0.014 0.98891
stenose70-90% -1.473e+01 9.481e+02 -0.016 0.98760
stenose90-99% -1.437e+01 9.481e+02 -0.015 0.98791
stenose100% (Occlusion) -1.476e-01 1.228e+03 0.000 0.99990
stenose50-99% -1.613e+01 9.481e+02 -0.017 0.98642
stenose70-99% -7.880e-01 1.183e+03 -0.001 0.99947
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 726.94 on 1037 degrees of freedom
Residual deviance: 673.02 on 1018 degrees of freedom
AIC: 713.02
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.306293
Standard error............: 0.112242
Odds ratio (effect size)..: 1.358
Lower 95% CI..............: 1.09
Upper 95% CI..............: 1.693
Z-value...................: 2.728861
P-value...................: 0.006355351
Hosmer and Lemeshow r^2...: 0.074179
Cox and Snell r^2.........: 0.050624
Nagelkerke's pseudo r^2...: 0.100528
Sample size of AE DB......: 2423
Sample size of model......: 1038
Missing data %............: 57.16054
Analysis of MCP1_rank.
- processing AsymptSympt
Call: glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
ORdate_year + Med.Statin.LLD, family = binomial(link = "logit"),
data = currentDF)
Coefficients:
(Intercept) currentDF[, PROTEIN] ORdate_year Med.Statin.LLDyes
-529.0018 0.3015 0.2650 -0.4436
Degrees of Freedom: 497 Total (i.e. Null); 494 Residual
Null Deviance: 442.3
Residual Deviance: 431.4 AIC: 439.4
Call:
glm(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, family = binomial(link = "logit"),
data = currentDF)
Deviance Residuals:
Min 1Q Median 3Q Max
-2.4505 0.3657 0.5162 0.6508 1.2570
Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -5.467e+02 1.385e+03 -0.395 0.69306
currentDF[, PROTEIN] 3.487e-01 1.293e-01 2.697 0.00701 **
Age 1.924e-02 1.635e-02 1.177 0.23928
Gendermale -3.424e-01 2.977e-01 -1.150 0.25001
ORdate_year 2.808e-01 1.220e-01 2.301 0.02139 *
Hypertension.compositeyes -5.366e-01 4.410e-01 -1.217 0.22366
DiabetesStatusDiabetes 1.954e-01 3.251e-01 0.601 0.54780
SmokerStatusEx-smoker -1.745e-01 2.861e-01 -0.610 0.54197
SmokerStatusNever smoked -4.281e-01 4.090e-01 -1.047 0.29516
Med.Statin.LLDyes -3.682e-01 3.131e-01 -1.176 0.23961
Med.all.antiplateletyes -4.933e-01 5.140e-01 -0.960 0.33722
GFR_MDRD 9.396e-03 7.081e-03 1.327 0.18455
BMI 1.197e-02 3.425e-02 0.349 0.72676
MedHx_CVDyes 8.287e-02 2.647e-01 0.313 0.75424
stenose50-70% -1.392e+01 1.363e+03 -0.010 0.99185
stenose70-90% -1.531e+01 1.363e+03 -0.011 0.99104
stenose90-99% -1.494e+01 1.363e+03 -0.011 0.99126
stenose100% (Occlusion) -8.723e-02 1.712e+03 0.000 0.99996
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 442.26 on 497 degrees of freedom
Residual deviance: 417.29 on 480 degrees of freedom
AIC: 453.29
Number of Fisher Scoring iterations: 15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' AsymptSympt ' ...
Collecting data...
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: AsymptSympt
Effect size...............: 0.348715
Standard error............: 0.129319
Odds ratio (effect size)..: 1.417
Lower 95% CI..............: 1.1
Upper 95% CI..............: 1.826
Z-value...................: 2.696545
P-value...................: 0.007006285
Hosmer and Lemeshow r^2...: 0.056471
Cox and Snell r^2.........: 0.048913
Nagelkerke's pseudo r^2...: 0.083108
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
C. Longitudinal analysis secondary clinical outcome
For the longitudinal analyses of plaque and plasma MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period.
The primary outcome is defined as “a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death”, i.e. major adverse cardiovascular events (MACE). Variable: epmajor.3years, these include: - myocardial infarction (MI) - cerebral infarction (CVA/stroke) - cardiovascular death (exact cause to be investigated) - cerebral bleeding (CVA/stroke) - fatal myocardial infarction (MI) - fatal cerebral infarction - fatal cerebral bleeding - sudden death - fatal heart failure - fatal aneurysm rupture - other cardiovascular death..
The secondary outcomes will be
- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable:
epstroke.3years, these include:- cerebral infarction (CVA/stroke)
- cerebral bleeding (CVA/stroke)
- fatal cerebral infarction
- fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable:
epcoronary.3years, these include:- myocardial infarction (MI)
- coronary angioplasty (PCI/PTCA)
- cardiovascular death (exact cause to be investigated)
- coronary bypass (CABG)
- fatal myocardial infarction (MI)
- sudden death.
- cardiovascular death - variable:
epcvdeath.3years, these include:- cardiovascular death (exact cause to be investigated)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..
30- and 90-days FU events
We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up ‘time-to-event’ variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up.
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90
# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
mutate(
FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"max.followup",
"FU.cutt.off.3years",
"FU.cutt.off.30days",
"FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
AEDB.CEA <- AEDB.CEA %>%
mutate(
FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
)
AEDB.CEA.temp <- subset(AEDB.CEA, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"max.followup",
"FU.cutt.off.3years",
"FU.cutt.off.30days",
"FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
rm(AEDB.CEA.temp)
Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:
- MACE (
epmajor.3years) - Stroke (
epstroke.3years) - Coronary events (
epcoronary.3years) - Cardiovascular death (
epcvdeath.3years)
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
mutate(
ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
)
AEDB.CEA <- AEDB.CEA %>%
mutate(
ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled,
ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
)
attach(AEDB)The following objects are masked from AEDB.CEA:
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_ng_ml_2015,
Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q, AgeSQR, aid, AlcoholUse, Aldosteron_recode, alg10201, alg10202, alg10203,
alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3, analgeti, Ang2, angioii, ANGPT2,
anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2, AP_Dx, AP_Dx1, AP_Dx2, APOB, artercon,
Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI, BMI_US, BMI_WHO, BMI30ormore, brain401,
brain402, brain403, brain404, brain405, brain406, brain407, brain408, brain409, brain410, brain411, brain412, brain413, brn40701,
bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history, CADPAOD_history, Calc.bin, calcification, calcium, calcium2, calreg, carbasal,
cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS, CEL, CFD_recalc, cholverl, cholverl2, cholverl3, CI_history, clau1, clau2,
Claudication, clopidog, CML, collagen, Collagen.bin, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2, concablo3, concace2,
concacei, concacet, concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc,
conccalc2, conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3,
conccort, conccorthorm2, concderm, concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye,
concgluc, concgluc2, concgluc3, concgluc4, concgrel, concinsu, conciron, conciron2, concneur, concneur2, concneur3, concneur4,
concnitr, concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren, concresp, concrheu, concrheu2, concrheu3,
concsta2, concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos, cortihorm2, creat,
crp_all, CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst,
dateapprox1, dateapprox2, dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest,
dateexact_worst, dateexact1, dateexact2, dateexact3, dateexact4, dateok, dermacor, DiabetesStatus, diastoli, diet801, diet802,
diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813, diet814, diet815, diet816, diet817,
diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, EGR, EMMPRIN_45kD, EMMPRIN_58kD, ENDOGLIN,
endpoint1, endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, EP_CAD, ep_cad_t_30days, ep_cad_t_3years, EP_CAD_time,
ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years, EP_composite, EP_composite_time,
EP_coronary, ep_coronary_t_30days, ep_coronary_t_3years, ep_coronary_t_90days, EP_coronary_time, EP_CVdeath, ep_cvdeath_t_30days,
ep_cvdeath_t_3years, ep_cvdeath_t_90days, EP_CVdeath_time, EP_death, ep_death_t_30days, ep_death_t_3years, EP_death_time, EP_fatalCVA,
ep_fatalCVA_t_30days, ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke, ep_hemorrhagic_stroke_t_3years,
EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke, ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time,
ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time, ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major,
ep_major_t_30days, ep_major_t_3years, ep_major_t_90days, EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time,
EP_nonstroke_event, EP_nonstroke_event_time, ep_nonstroke_t_3years, EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years,
EP_peripheral_time, EP_pta, ep_pta_t_30days, ep_pta_t_3years, EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years,
ep_stroke_t_90days, EP_stroke_time, EP_strokeCVdeath, ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time,
EP_strokedeath, ep_strokedeath_t_30days, ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking, epcad.3years, epci.30days,
epci.3years, epcom.30days, epcom.3years, epcoronary.30days, epcoronary.3years, epcoronary.90days, epcvdeath.30days, epcvdeath.3years,
epcvdeath.90days, epdeath.30days, epdeath.3years, epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days,
eplegamputation.3years, epmajor.30days, epmajor.3years, epmajor.90days, epmi.30days, epmi.3years, epnonstroke.3years,
epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years, epstroke.30days, epstroke.3years, epstroke.90days,
epstrokeCVdeath.30days, epstrokeCVdeath.3years, epstrokedeath.30days, epstrokedeath.3years, erec, Estradiol, everstroke_composite,
Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905, exer906, exer9071, exer9072, exer9073, exer9074, exer9075,
exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4, FABP4_pg_ug, FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40,
Femoral.interv, FH_AAA_broth, FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth,
FH_amp_comp, FH_amp_mat, FH_amp_parent, FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent,
FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis, FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat,
FH_corcalc_sibling, FH_corcalc_sis, FH_CVD_broth, FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis,
FH_CVdeath_broth, FH_CVdeath_comp, FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth,
FH_DM_comp, FH_DM_mat, FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat,
FH_HC_sibling, FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth,
FH_MI_comp, FH_MI_mat, FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat,
FH_otherCVD_parent, FH_otherCVD_pat, FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent,
FH_PAD_pat, FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis,
FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp,
FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat,
FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent,
FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date,
FU.cutt.off.30days, FU.cutt.off.3years, FU.cutt.off.90days, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med,
Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb,
HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301,
heart302, heart303, heart304, heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314,
heart315, heart316, heart317, heart318, heart319, heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327,
heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON, Hypertension.composite, Hypertension.drugs,
Hypertension.selfreport, Hypertension.selfreportdrug, Hypertension1, Hypertension2, IL1_Beta, IL10, IL12, IL13, IL17, IL2, IL21, IL4,
IL5, IL6, IL6_pg_ml_2015, IL6_pg_ug_2015, IL6R_pg_ml_2015, IL6R_pg_ug_2015, IL8, IL8_pg_ml_2015, IL8_pg_ug_2015, IL9,
indexsymptoms_latest, indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, informedconsent, insulin, insuline,
INVULDAT, IP10, IPH_extended.bin, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg, LDL_clinic, LDL_dif,
LDL_final, LDL_finalCU, ldl_source, LDL_var, leg501, leg502, leg503, leg504, leg505, leg506, leg507, leg508, leg509, leg510, leg511,
leg512, leg513, leg514, leg515, leg516, leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R, macmean0, macrophages,
macrophages_location, Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1, MCP1_pg_ml_2015, MCP1_pg_ug_2015,
MCP1_plasma_olink, MCP1_plasma_olink_rankNorm, MCSF_pg_ml_2015, MCSF_pg_ug_2015, MDC, Med_notes, Med.ablock, Med.ACE_inh,
Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3, Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag,
Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag, Med.dipyridamole, Med.diuretic,
Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, MedHx_CVD, media,
MG_H1, MI_Dx, MI_Dx1, MI_Dx2, MIF, MIG, MIP1a, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19, miRNA155_RNU48, MMP14, MMP2, MMP2TIMP2,
MMP8, MMP9, MMP9TIMP1, MPO_plasma, MRP_14, MRP_8, MRP_8_14C, MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl,
neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5, neutrophils, NGAL, NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local,
NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1, NOD2, nogobt1_recalculated, NTproBNP_plasma, Number_Events_Sorter,
Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells, Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705,
oac706, oac707, oac708, oac709, oac710, oac711, oac712, oac713, oac714, OKyear, OPG, OPG_plasma, OPN, OPN_2013, OPN_plasma, OR_blood,
Oral.glucose.inh, oralgluc, oralgluc2, oralgluc3, oralgluc4, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype,
PAI1_pg_ml_2015, PAI1_pg_ug_2015, PAOD, PARC, patch, PCSK9_plasma, PDGF_BB_plasma, Percentage_CD14, Percentage_CD20, Percentage_CD4,
Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, plaquephenotype, PlateID_plasma_olink, positibl, PrimaryLast, PrimaryLast1,
prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306, qual0307, qual0308, qual0309,
qual0310, qual0401, qual0402, qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08, qual0901, qual0902, qual0903,
qual0904, qual0905, qual0906, qual0907, qual0908, qual0909, qual1010, qual1101, qual1102, qual1103, qual1104, RAAS_med, RANTES,
RANTES_pg_ml_2015, RANTES_pg_ug_2015, RANTES_plasma, Ras, RE50_01, RE70_01, Renine_recode, renineinh, restenos, restenosisOK, rheuma,
rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608, risk609, risk610, risk611, risk612, risk613,
risk614, risk615, risk616, risk617, risk618, risk619, risk620, Segment_isolated_Tris_2015, SHBG, sICAM1, SMAD1_5_8, SMAD2, SMAD3, smc,
smc_location, smc_macrophages_ratio, SMC.bin, smcmean0, SmokerCurrent, SmokerStatus, SmokingReported, SmokingYearOR, stat3P, statin2,
statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral, Stenosis_ipsilateral, Stroke_Dx,
Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER, sympt,
Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC,
TAT_plasma, TC_2016, TC_all, TC_avg, TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all, TG_avg,
TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, thrombos, thrombus, thrombus_location, thrombus_new,
thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, totalchol, totalcholesterol_source, tractdig, tractdig2,
tractdig3, tractdig4, tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Tris_protein_conc_ug_ml_2015, Trop1, Trop1DT,
Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT, tropomaxpositief, TSratio_blood, TSratio_plaque, UPID,
validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001, VEGFA_plasma, vegfa422,
vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012, vessel_density_Timo2012_2,
vessel_density_Timo2013, vitamin, vitamin2, vitb12, VRAGENLIJST, vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation,
writtenIC, yearablo, yearablo2, yearablo3, yearace, yearace2, yearacet, yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2,
yearanti, yearanti2, yearbblo, yearbblo2, yearcalc, yearcalc2, yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1,
yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort, yearcorthorm2, yearderm, yeardig, yeardig2, yeardig3, yeardig4, yeardipy,
yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc, yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu, yeariron, yeariron2,
yearneur, yearneur2, yearneur3, yearneur4, yearnitr, yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2,
yearothe, yearpros, yearpsy5, yearren, yearresp, yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2,
yearvit2, yearvita, Yrs.no.smoking, Yrs.smokingAEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0
AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0
AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0
AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0
AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0
AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0
AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0
AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0
detach(AEDB)
AEDB.temp <- subset(AEDB, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
"epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
"epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
rm(AEDB.temp)
attach(AEDB.CEA)The following objects are masked from AEDB.CEA (pos = 3):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_ng_ml_2015,
Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q, AgeGroup, AgeGroupSex, AgeSQR, aid, AlcoholUse, Aldosteron_recode,
alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2,
AP_Dx, AP_Dx1, AP_Dx2, APOB, artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI,
BMI_US, BMI_WHO, BMI30ormore, BMIGroup, brain401, brain402, brain403, brain404, brain405, brain406, brain407, brain408, brain409,
brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history, CADPAOD_history, Calc.bin,
calcification, CalcificationPlaque, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS,
CEL, CFD_recalc, cholverl, cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, COL_Instability, collagen,
Collagen.bin, CollagenPlaque, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2, concablo3, concace2, concacei, concacet,
concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc, conccalc2,
conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort,
conccorthorm2, concderm, concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc,
concgluc2, concgluc3, concgluc4, concgrel, concinsu, conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr,
concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren, concresp, concrheu, concrheu2, concrheu3, concsta2,
concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos, cortihorm2, creat, crp_all,
CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst,
dateapprox1, dateapprox2, dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest,
dateexact_worst, dateexact1, dateexact2, dateexact3, dateexact4, dateok, dermacor, DiabetesStatus, diastoli, diet801, diet802,
diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813, diet814, diet815, diet816, diet817,
diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, eGFRGroup, EGR, EMMPRIN_45kD, EMMPRIN_58kD,
ENDOGLIN, endpoint1, endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, Eotaxin1_rank, EP_CAD, ep_cad_t_30days,
ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years,
EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_30days, ep_coronary_t_3years, ep_coronary_t_90days, EP_coronary_time,
EP_CVdeath, ep_cvdeath_t_30days, ep_cvdeath_t_3years, ep_cvdeath_t_90days, EP_CVdeath_time, EP_death, ep_death_t_30days,
ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke,
ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke,
ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time,
ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, ep_major_t_90days,
EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time, ep_nonstroke_t_3years,
EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days, ep_pta_t_3years,
EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, ep_stroke_t_90days, EP_stroke_time, EP_strokeCVdeath,
ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days,
ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking, epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years,
epcoronary.30days, epcoronary.3years, epcoronary.90days, epcvdeath.30days, epcvdeath.3years, epcvdeath.90days, epdeath.30days,
epdeath.3years, epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years,
epmajor.90days, epmi.30days, epmi.3years, epnonstroke.3years, epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years,
epstroke.30days, epstroke.3years, epstroke.90days, epstrokeCVdeath.30days, epstrokeCVdeath.3years, epstrokedeath.30days,
epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905,
exer906, exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4,
FABP4_pg_ug, FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40, FAT10_Instability, Fat10Perc, Femoral.interv, FH_AAA_broth,
FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp, FH_amp_mat, FH_amp_parent,
FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis,
FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis,
FH_CVD_broth, FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp,
FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat,
FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat, FH_HC_sibling,
FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat,
FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent,
FH_otherCVD_pat, FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat,
FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis,
FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp,
FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat,
FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent,
FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date,
FU.cutt.off.30days, FU.cutt.off.3years, FU.cutt.off.90days, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med,
Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb,
HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301,
heart302, heart303, heart304, heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314,
heart315, heart316, heart317, heart318, heart319, heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327,
heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON, Hypertension.composite, Hypertension.drugs,
Hypertension.selfreport, Hypertension.selfreportdrug, Hypertension1, Hypertension2, IL1_Beta, IL10, IL10_rank, IL12, IL12_rank, IL13,
IL13_rank, IL17, IL2, IL2_rank, IL21, IL21_rank, IL4, IL4_rank, IL5, IL5_rank, IL6, IL6_pg_ml_2015, IL6_pg_ug_2015, IL6_rank,
IL6R_pg_ml_2015, IL6R_pg_ug_2015, IL8, IL8_pg_ml_2015, IL8_pg_ug_2015, IL8_rank, IL9, IL9_rank, indexsymptoms_latest,
indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, INFG_rank, informedconsent, insulin, insuline, INVULDAT,
IP10, IP10_rank, IPH, IPH_extended.bin, IPH_Instability, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg,
LDL_clinic, LDL_dif, LDL_final, LDL_finalCU, ldl_source, LDL_var, LDLGroup, leg501, leg502, leg503, leg504, leg505, leg506, leg507,
leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516, leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R,
MAC_binned, MAC_grouped, MAC_Instability, macmean0, macrophages, Macrophages_LN, macrophages_location, Macrophages_rank,
Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1, MCP1_LN, MCP1_pg_ml_2015, MCP1_pg_ml_2015_LN, MCP1_pg_ml_2015_rank,
MCP1_pg_ug_2015, MCP1_pg_ug_2015_LN, MCP1_pg_ug_2015_rank, MCP1_plasma_olink, MCP1_plasma_olink_LN, MCP1_plasma_olink_rank,
MCP1_plasma_olink_rankNorm, MCP1_rank, MCSF_pg_ml_2015, MCSF_pg_ug_2015, MDC, MDC_rank, Med_notes, Med.ablock, Med.ACE_inh,
Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3, Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag,
Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag, Med.dipyridamole, Med.diuretic,
Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, MedHx_CVD, media,
MG_H1, MI_Dx, MI_Dx1, MI_Dx2, MIF, MIF_rank, MIG, MIG_rank, MIP1a, MIP1a_rank, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19,
miRNA155_RNU48, MMP14, MMP2, MMP2_rank, MMP2TIMP2, MMP8, MMP8_rank, MMP9, MMP9_rank, MMP9TIMP1, MPO_plasma, MRP_14, MRP_8, MRP_8_14C,
MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5,
neutrophils, NGAL, NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1,
NOD2, nogobt1_recalculated, NTproBNP_plasma, Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells,
Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706, oac707, oac708, oac709, oac710, oac711, oac712, oac713,
oac714, OKyear, OPG, OPG_plasma, OPG_rank, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2, oralgluc3,
oralgluc4, ORdate_epoch, ORdate_year, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ml_2015,
PAI1_pg_ug_2015, PAOD, PARC, PARC_rank, patch, PCSK9_plasma, PDGF_BB_plasma, Percentage_CD14, Percentage_CD20, Percentage_CD4,
Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, Plaque_Vulnerability_Index, plaquephenotype, PlateID_plasma_olink, positibl,
PrimaryLast, PrimaryLast1, prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306,
qual0307, qual0308, qual0309, qual0310, qual0401, qual0402, qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08,
qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909, qual1010, qual1101, qual1102, qual1103,
qual1104, RAAS_med, RANTES, RANTES_pg_ml_2015, RANTES_pg_ug_2015, RANTES_plasma, RANTES_rank, Ras, RE50_01, RE70_01, Renine_recode,
renineinh, restenos, restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608,
risk609, risk610, risk611, risk612, risk613, risk614, risk615, risk616, risk617, risk618, risk619, risk620, SBPGroup,
Segment_isolated_Tris_2015, SHBG, sICAM1, sICAM1_rank, SMAD1_5_8, SMAD2, SMAD3, smc, SMC_binned, SMC_grouped, SMC_Instability, SMC_LN,
smc_location, smc_macrophages_ratio, SMC_rank, SMC.bin, smcmean0, SmokerCurrent, SmokerStatus, SmokingReported, SmokingYearOR, stat3P,
statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral, Stenosis_ipsilateral, StenoticGroup,
Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER,
sympt, Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC,
TARC_rank, TAT_plasma, TC_2016, TC_all, TC_avg, TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all,
TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, TGFB_rank, thrombos, thrombus, thrombus_location, thrombus_new,
thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, TNFA_rank, totalchol, totalcholesterol_source, tractdig,
tractdig2, tractdig3, tractdig4, tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Tris_protein_conc_ug_ml_2015,
Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT, tropomaxpositief, TSratio_blood, TSratio_plaque,
UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001, VEGFA, VEGFA_plasma,
VEGFA_rank, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012,
vessel_density_Timo2012_2, vessel_density_Timo2013, VesselDensity_LN, VesselDensity_rank, vitamin, vitamin2, vitb12, VRAGENLIJST,
vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2, yearablo3, yearace, yearace2, yearacet,
yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc, yearcalc2,
yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort,
yearcorthorm2, yearderm, yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc,
yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu, yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr,
yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe, yearpros, yearpsy5, yearren, yearresp,
yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking, Yrs.smokingAEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0
AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0
AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0
detach(AEDB.CEA)
AEDB.CEA.temp <- subset(AEDB.CEA, select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary",
"epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
"epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
"epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)
DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)
rm(AEDB.CEA.temp)
Sanity checks
First we do some sanity checks and inventory the time-to-event and event variables.
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")
cat("* Creating function to summarize Cox regression and prepare container for results.")* Creating function to summarize Cox regression and prepare container for results.# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
if (nrow(summary(coxfit)$coefficients) == 1) {
output = c(protein, rep(NA,8))
cat("Model not fitted; probably singular.\n")
}else {
cat("Collecting data.\n\n")
cox.sum <- summary(coxfit)
cox.effectsize = cox.sum$coefficients[1,1]
cox.SE = cox.sum$coefficients[1,3]
cox.HReffect = cox.sum$coefficients[1,2]
cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
cox.zvalue = cox.sum$coefficients[1,4]
cox.pvalue = cox.sum$coefficients[1,5]
cox.sample_size = cox.sum$n
cox.nevents = cox.sum$nevent
output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
cat("We have collected the following:\n")
cat("Dataset used..............:", DATASET, "\n")
cat("Outcome analyzed..........:", OUTCOME, "\n")
cat("Protein...................:", protein, "\n")
cat("Effect size...............:", round(cox.effectsize, 6), "\n")
cat("Standard error............:", round(cox.SE, 6), "\n")
cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
cat("T-value...................:", round(cox.zvalue, 6), "\n")
cat("P-value...................:", signif(cox.pvalue, 8), "\n")
cat("Sample size in model......:", cox.sample_size, "\n")
cat("Number of events..........:", cox.nevents, "\n")
}
return(output)
print(output)
}
times = c("ep_major_t_3years",
"ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")
endpoints = c("epmajor.3years",
"epstroke.3years", "epcoronary.3years", "epcvdeath.3years")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints){
require(labelled)
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.3years"
0 1
2035 265
[1] "Printing the summary of: epstroke.3years"
0 1
2171 130
[1] "Printing the summary of: epcoronary.3years"
0 1
2119 182
[1] "Printing the summary of: epcvdeath.3years"
0 1
2210 90 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_3years"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.710 3.000 2.573 3.000 3.000 125
[1] "Printing the summary of: ep_stroke_t_3years"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.877 3.000 2.624 3.000 3.000 125
[1] "Printing the summary of: ep_coronary_t_3years"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.000 2.784 3.000 2.622 3.000 3.000 125
[1] "Printing the summary of: ep_cvdeath_t_3years"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00274 2.91233 3.00000 2.70902 3.00000 3.00000 125 for (eventtime in times){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_3years"
[1] "Printing the distribution of: ep_stroke_t_3years"
[1] "Printing the distribution of: ep_coronary_t_3years"
[1] "Printing the distribution of: ep_cvdeath_t_3years"
times30 = c("ep_major_t_30days",
"ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")
endpoints30 = c("epmajor.30days",
"epstroke.30days", "epcoronary.30days", "epcvdeath.30days")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints30){
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.30days"
0 1
2222 78
[1] "Printing the summary of: epstroke.30days"
0 1
2248 53
[1] "Printing the summary of: epcoronary.30days"
0 1
2267 34
[1] "Printing the summary of: epcvdeath.30days"
0 1
2288 12 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times30){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_30days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 30.00 30.00 29.09 30.00 30.00 125
[1] "Printing the summary of: ep_stroke_t_30days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 30.00 30.00 29.32 30.00 30.00 125
[1] "Printing the summary of: ep_coronary_t_30days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 30.00 30.00 29.54 30.00 30.00 125
[1] "Printing the summary of: ep_cvdeath_t_30days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.001 30.000 30.000 29.854 30.000 30.000 125 for (eventtime in times30){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_30days"
[1] "Printing the distribution of: ep_stroke_t_30days"
[1] "Printing the distribution of: ep_coronary_t_30days"
[1] "Printing the distribution of: ep_cvdeath_t_30days"
times90 = c("ep_major_t_90days",
"ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")
endpoints90 = c("epmajor.90days",
"epstroke.90days", "epcoronary.90days", "epcvdeath.90days")
cat("* Check the cases per event type - for sanity.")* Check the cases per event type - for sanity.for (events in endpoints90){
print(paste0("Printing the summary of: ",events))
# print(summary(AEDB.CEA[,events]))
print(table(AEDB.CEA[,events]))
}[1] "Printing the summary of: epmajor.90days"
0 1
2206 94
[1] "Printing the summary of: epstroke.90days"
0 1
2241 60
[1] "Printing the summary of: epcoronary.90days"
0 1
2257 44
[1] "Printing the summary of: epcvdeath.90days"
0 1
2281 19 cat("* Check distribution of events over time - for sanity.")* Check distribution of events over time - for sanity.for (eventtimes in times90){
print(paste0("Printing the summary of: ",eventtimes))
print(summary(AEDB.CEA[,eventtimes]))
}[1] "Printing the summary of: ep_major_t_90days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 90.00 90.00 86.75 90.00 90.00 125
[1] "Printing the summary of: ep_stroke_t_90days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 90.00 90.00 87.51 90.00 90.00 125
[1] "Printing the summary of: ep_coronary_t_90days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.00 90.00 90.00 88.21 90.00 90.00 125
[1] "Printing the summary of: ep_cvdeath_t_90days"
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
1.001 90.000 90.000 89.320 90.000 90.000 125 for (eventtime in times90){
print(paste0("Printing the distribution of: ",eventtime))
p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
main = eventtime, bins = 15,
xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal())
print(p)
ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}[1] "Printing the distribution of: ep_major_t_90days"
[1] "Printing the distribution of: ep_stroke_t_90days"
[1] "Printing the distribution of: ep_coronary_t_90days"
[1] "Printing the distribution of: ep_cvdeath_t_90days"
Cox regressions
Let’s perform the actual Cox-regressions. We will apply a couple of models:
- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
3 years follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 4 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1186, number of events= 139
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 0.09565 1.10037 0.17845 0.536 0.591959
Age 0.03515 1.03577 0.01006 3.493 0.000477 ***
Gendermale 0.34053 1.40569 0.19972 1.705 0.088184 .
ORdate_year -0.03129 0.96920 0.02958 -1.058 0.290193
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 1.1004 0.9088 0.7756 1.561
Age 1.0358 0.9655 1.0155 1.056
Gendermale 1.4057 0.7114 0.9504 2.079
ORdate_year 0.9692 1.0318 0.9146 1.027
Concordance= 0.591 (se = 0.026 )
Likelihood ratio test= 16.26 on 4 df, p=0.003
Wald test = 15.38 on 4 df, p=0.004
Score (logrank) test = 15.46 on 4 df, p=0.004
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.095651
Standard error............: 0.178454
Odds ratio (effect size)..: 1.1
Lower 95% CI..............: 0.776
Upper 95% CI..............: 1.561
T-value...................: 0.535999
P-value...................: 0.5919592
Sample size in model......: 1186
Number of events..........: 139
> processing [MCP1_pg_ml_2015_rank]; 2 out of 4 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1187, number of events= 140
(1236 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 0.01185 1.01192 0.18373 0.064 0.948575
Age 0.03489 1.03550 0.01003 3.478 0.000506 ***
Gendermale 0.35203 1.42196 0.20065 1.754 0.079351 .
ORdate_year -0.02361 0.97667 0.03018 -0.782 0.434149
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.0119 0.9882 0.7059 1.451
Age 1.0355 0.9657 1.0153 1.056
Gendermale 1.4220 0.7033 0.9596 2.107
ORdate_year 0.9767 1.0239 0.9206 1.036
Concordance= 0.589 (se = 0.025 )
Likelihood ratio test= 16.08 on 4 df, p=0.003
Wald test = 15.15 on 4 df, p=0.004
Score (logrank) test = 15.23 on 4 df, p=0.004
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.01185
Standard error............: 0.18373
Odds ratio (effect size)..: 1.012
Lower 95% CI..............: 0.706
Upper 95% CI..............: 1.451
T-value...................: 0.064496
P-value...................: 0.9485755
Sample size in model......: 1187
Number of events..........: 140
> processing [MCP1_rank]; 3 out of 4 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 549, number of events= 70
(1874 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -0.22427 0.79910 0.24647 -0.910 0.3629
Age 0.02639 1.02674 0.01475 1.789 0.0736 .
Gendermale 0.87183 2.39128 0.34246 2.546 0.0109 *
ORdate_year -0.03519 0.96542 0.11300 -0.311 0.7555
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 0.7991 1.2514 0.4929 1.295
Age 1.0267 0.9740 0.9975 1.057
Gendermale 2.3913 0.4182 1.2222 4.679
ORdate_year 0.9654 1.0358 0.7736 1.205
Concordance= 0.618 (se = 0.034 )
Likelihood ratio test= 12.21 on 4 df, p=0.02
Wald test = 10.74 on 4 df, p=0.03
Score (logrank) test = 11.16 on 4 df, p=0.02
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_rank
Effect size...............: -0.224272
Standard error............: 0.246475
Odds ratio (effect size)..: 0.799
Lower 95% CI..............: 0.493
Upper 95% CI..............: 1.295
T-value...................: -0.909918
P-value...................: 0.3628658
Sample size in model......: 549
Number of events..........: 70
> processing [MCP1_plasma_olink_rank]; 4 out of 4 proteins.
> cross tabulation of MCP1_plasma_olink_rank-stratum.
[-3.18339,0.00365) [ 0.00365,3.18339]
344 343
> fitting the model for MCP1_plasma_olink_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 687, number of events= 128
(1736 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 0.61630 1.85207 0.19112 3.225 0.00126 **
Age 0.01986 1.02006 0.01069 1.858 0.06317 .
Gendermale 0.25065 1.28486 0.20560 1.219 0.22280
ORdate_year -0.01544 0.98467 0.02221 -0.695 0.48689
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 1.8521 0.5399 1.2734 2.694
Age 1.0201 0.9803 0.9989 1.042
Gendermale 1.2849 0.7783 0.8587 1.923
ORdate_year 0.9847 1.0156 0.9427 1.028
Concordance= 0.622 (se = 0.023 )
Likelihood ratio test= 21.62 on 4 df, p=2e-04
Wald test = 20.58 on 4 df, p=4e-04
Score (logrank) test = 21.33 on 4 df, p=3e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_plasma_olink_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_plasma_olink_rank
Effect size...............: 0.616301
Standard error............: 0.191122
Odds ratio (effect size)..: 1.852
Lower 95% CI..............: 1.273
Upper 95% CI..............: 2.694
T-value...................: 3.224648
P-value...................: 0.001261279
Sample size in model......: 687
Number of events..........: 128
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 4 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1186, number of events= 73
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 0.25410 1.28931 0.24572 1.034 0.30108
Age 0.03783 1.03856 0.01391 2.721 0.00651 **
Gendermale 0.06641 1.06867 0.25908 0.256 0.79769
ORdate_year -0.06407 0.93794 0.04088 -1.567 0.11704
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 1.2893 0.7756 0.7965 2.087
Age 1.0386 0.9629 1.0106 1.067
Gendermale 1.0687 0.9357 0.6431 1.776
ORdate_year 0.9379 1.0662 0.8657 1.016
Concordance= 0.605 (se = 0.035 )
Likelihood ratio test= 9.61 on 4 df, p=0.05
Wald test = 9.21 on 4 df, p=0.06
Score (logrank) test = 9.29 on 4 df, p=0.05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.254104
Standard error............: 0.24572
Odds ratio (effect size)..: 1.289
Lower 95% CI..............: 0.797
Upper 95% CI..............: 2.087
T-value...................: 1.03412
P-value...................: 0.3010799
Sample size in model......: 1186
Number of events..........: 73
> processing [MCP1_pg_ml_2015_rank]; 2 out of 4 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1187, number of events= 74
(1236 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 0.05159 1.05295 0.25152 0.205 0.83747
Age 0.03709 1.03779 0.01382 2.684 0.00728 **
Gendermale 0.09193 1.09629 0.26020 0.353 0.72385
ORdate_year -0.04704 0.95405 0.04159 -1.131 0.25806
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.053 0.9497 0.6432 1.724
Age 1.038 0.9636 1.0101 1.066
Gendermale 1.096 0.9122 0.6583 1.826
ORdate_year 0.954 1.0482 0.8794 1.035
Concordance= 0.591 (se = 0.035 )
Likelihood ratio test= 8.33 on 4 df, p=0.08
Wald test = 7.9 on 4 df, p=0.1
Score (logrank) test = 7.96 on 4 df, p=0.09
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.051594
Standard error............: 0.251515
Odds ratio (effect size)..: 1.053
Lower 95% CI..............: 0.643
Upper 95% CI..............: 1.724
T-value...................: 0.205134
P-value...................: 0.8374673
Sample size in model......: 1187
Number of events..........: 74
> processing [MCP1_rank]; 3 out of 4 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 549, number of events= 36
(1874 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -0.366525 0.693139 0.345901 -1.060 0.289
Age 0.007526 1.007554 0.019822 0.380 0.704
Gendermale 0.332937 1.395059 0.403044 0.826 0.409
ORdate_year -0.014799 0.985310 0.157445 -0.094 0.925
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 0.6931 1.4427 0.3519 1.365
Age 1.0076 0.9925 0.9692 1.047
Gendermale 1.3951 0.7168 0.6332 3.074
ORdate_year 0.9853 1.0149 0.7237 1.341
Concordance= 0.571 (se = 0.043 )
Likelihood ratio test= 1.96 on 4 df, p=0.7
Wald test = 1.92 on 4 df, p=0.8
Score (logrank) test = 1.93 on 4 df, p=0.7
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_rank
Effect size...............: -0.366525
Standard error............: 0.345901
Odds ratio (effect size)..: 0.693
Lower 95% CI..............: 0.352
Upper 95% CI..............: 1.365
T-value...................: -1.059623
P-value...................: 0.2893161
Sample size in model......: 549
Number of events..........: 36
> processing [MCP1_plasma_olink_rank]; 4 out of 4 proteins.
> cross tabulation of MCP1_plasma_olink_rank-stratum.
[-3.18339,0.00365) [ 0.00365,3.18339]
344 343
> fitting the model for MCP1_plasma_olink_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 687, number of events= 66
(1736 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 0.47352 1.60563 0.26362 1.796 0.0725 .
Age 0.03652 1.03720 0.01514 2.413 0.0158 *
Gendermale 0.12316 1.13107 0.27805 0.443 0.6578
ORdate_year -0.03125 0.96923 0.03121 -1.001 0.3166
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 1.6056 0.6228 0.9578 2.692
Age 1.0372 0.9641 1.0069 1.068
Gendermale 1.1311 0.8841 0.6559 1.951
ORdate_year 0.9692 1.0317 0.9117 1.030
Concordance= 0.626 (se = 0.034 )
Likelihood ratio test= 13.32 on 4 df, p=0.01
Wald test = 12.59 on 4 df, p=0.01
Score (logrank) test = 12.9 on 4 df, p=0.01
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_plasma_olink_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_plasma_olink_rank
Effect size...............: 0.473519
Standard error............: 0.263621
Odds ratio (effect size)..: 1.606
Lower 95% CI..............: 0.958
Upper 95% CI..............: 2.692
T-value...................: 1.796214
P-value...................: 0.07246048
Sample size in model......: 687
Number of events..........: 66
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 4 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1186, number of events= 91
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] -0.098282 0.906394 0.220619 -0.445 0.6560
Age 0.008967 1.009008 0.012039 0.745 0.4564
Gendermale 0.669615 1.953485 0.269234 2.487 0.0129 *
ORdate_year -0.040585 0.960228 0.036895 -1.100 0.2713
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 0.9064 1.1033 0.5882 1.397
Age 1.0090 0.9911 0.9855 1.033
Gendermale 1.9535 0.5119 1.1525 3.311
ORdate_year 0.9602 1.0414 0.8932 1.032
Concordance= 0.59 (se = 0.03 )
Likelihood ratio test= 9.17 on 4 df, p=0.06
Wald test = 8.27 on 4 df, p=0.08
Score (logrank) test = 8.5 on 4 df, p=0.07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.098282
Standard error............: 0.220619
Odds ratio (effect size)..: 0.906
Lower 95% CI..............: 0.588
Upper 95% CI..............: 1.397
T-value...................: -0.445482
P-value...................: 0.6559715
Sample size in model......: 1186
Number of events..........: 91
> processing [MCP1_pg_ml_2015_rank]; 2 out of 4 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1187, number of events= 91
(1236 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 0.172342 1.188084 0.225648 0.764 0.4450
Age 0.008689 1.008727 0.012048 0.721 0.4708
Gendermale 0.643664 1.903442 0.270491 2.380 0.0173 *
ORdate_year -0.055903 0.945631 0.037238 -1.501 0.1333
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.1881 0.8417 0.7634 1.849
Age 1.0087 0.9913 0.9852 1.033
Gendermale 1.9034 0.5254 1.1202 3.234
ORdate_year 0.9456 1.0575 0.8791 1.017
Concordance= 0.591 (se = 0.031 )
Likelihood ratio test= 9.57 on 4 df, p=0.05
Wald test = 8.73 on 4 df, p=0.07
Score (logrank) test = 8.95 on 4 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.172342
Standard error............: 0.225648
Odds ratio (effect size)..: 1.188
Lower 95% CI..............: 0.763
Upper 95% CI..............: 1.849
T-value...................: 0.763763
P-value...................: 0.4450084
Sample size in model......: 1187
Number of events..........: 91
> processing [MCP1_rank]; 3 out of 4 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 549, number of events= 46
(1874 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 0.24448 1.27695 0.30911 0.791 0.4290
Age 0.03668 1.03736 0.01872 1.959 0.0501 .
Gendermale 0.92420 2.51986 0.43913 2.105 0.0353 *
ORdate_year -0.23892 0.78748 0.13604 -1.756 0.0790 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 1.2770 0.7831 0.6967 2.340
Age 1.0374 0.9640 1.0000 1.076
Gendermale 2.5199 0.3968 1.0656 5.959
ORdate_year 0.7875 1.2699 0.6032 1.028
Concordance= 0.652 (se = 0.039 )
Likelihood ratio test= 13.98 on 4 df, p=0.007
Wald test = 12.67 on 4 df, p=0.01
Score (logrank) test = 13.17 on 4 df, p=0.01
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_rank
Effect size...............: 0.244478
Standard error............: 0.309109
Odds ratio (effect size)..: 1.277
Lower 95% CI..............: 0.697
Upper 95% CI..............: 2.34
T-value...................: 0.79091
P-value...................: 0.4289965
Sample size in model......: 549
Number of events..........: 46
> processing [MCP1_plasma_olink_rank]; 4 out of 4 proteins.
> cross tabulation of MCP1_plasma_olink_rank-stratum.
[-3.18339,0.00365) [ 0.00365,3.18339]
344 343
> fitting the model for MCP1_plasma_olink_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 687, number of events= 75
(1736 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 0.62072 1.86026 0.24754 2.508 0.0122 *
Age -0.01376 0.98634 0.01364 -1.008 0.3133
Gendermale 0.62406 1.86650 0.29102 2.144 0.0320 *
ORdate_year -0.01402 0.98608 0.02874 -0.488 0.6256
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 1.8603 0.5376 1.1452 3.022
Age 0.9863 1.0139 0.9603 1.013
Gendermale 1.8665 0.5358 1.0551 3.302
ORdate_year 0.9861 1.0141 0.9321 1.043
Concordance= 0.609 (se = 0.031 )
Likelihood ratio test= 12.5 on 4 df, p=0.01
Wald test = 11.55 on 4 df, p=0.02
Score (logrank) test = 11.9 on 4 df, p=0.02
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_plasma_olink_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_plasma_olink_rank
Effect size...............: 0.620717
Standard error............: 0.247542
Odds ratio (effect size)..: 1.86
Lower 95% CI..............: 1.145
Upper 95% CI..............: 3.022
T-value...................: 2.507523
P-value...................: 0.01215806
Sample size in model......: 687
Number of events..........: 75
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 4 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1186, number of events= 45
(1237 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 0.02251 1.02276 0.31172 0.072 0.9424
Age 0.09026 1.09446 0.02010 4.491 7.1e-06 ***
Gendermale 0.89781 2.45423 0.41218 2.178 0.0294 *
ORdate_year -0.07622 0.92661 0.05281 -1.443 0.1489
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 1.0228 0.9777 0.5552 1.884
Age 1.0945 0.9137 1.0522 1.138
Gendermale 2.4542 0.4075 1.0941 5.505
ORdate_year 0.9266 1.0792 0.8355 1.028
Concordance= 0.715 (se = 0.039 )
Likelihood ratio test= 28.96 on 4 df, p=8e-06
Wald test = 24.48 on 4 df, p=6e-05
Score (logrank) test = 25.34 on 4 df, p=4e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.022505
Standard error............: 0.311719
Odds ratio (effect size)..: 1.023
Lower 95% CI..............: 0.555
Upper 95% CI..............: 1.884
T-value...................: 0.072197
P-value...................: 0.9424454
Sample size in model......: 1186
Number of events..........: 45
> processing [MCP1_pg_ml_2015_rank]; 2 out of 4 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 1187, number of events= 45
(1236 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] -0.11518 0.89120 0.32234 -0.357 0.7208
Age 0.09047 1.09469 0.02008 4.505 6.63e-06 ***
Gendermale 0.91435 2.49514 0.41402 2.208 0.0272 *
ORdate_year -0.06875 0.93356 0.05424 -1.267 0.2050
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 0.8912 1.1221 0.4738 1.676
Age 1.0947 0.9135 1.0524 1.139
Gendermale 2.4951 0.4008 1.1084 5.617
ORdate_year 0.9336 1.0712 0.8394 1.038
Concordance= 0.716 (se = 0.039 )
Likelihood ratio test= 29.09 on 4 df, p=7e-06
Wald test = 24.68 on 4 df, p=6e-05
Score (logrank) test = 25.41 on 4 df, p=4e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: -0.115182
Standard error............: 0.322341
Odds ratio (effect size)..: 0.891
Lower 95% CI..............: 0.474
Upper 95% CI..............: 1.676
T-value...................: -0.357328
P-value...................: 0.7208462
Sample size in model......: 1187
Number of events..........: 45
> processing [MCP1_rank]; 3 out of 4 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 549, number of events= 26
(1874 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -0.03367 0.96689 0.40417 -0.083 0.9336
Age 0.05571 1.05729 0.02549 2.185 0.0289 *
Gendermale 1.05290 2.86594 0.61477 1.713 0.0868 .
ORdate_year -0.11039 0.89548 0.18082 -0.611 0.5415
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 0.9669 1.0342 0.4379 2.135
Age 1.0573 0.9458 1.0058 1.111
Gendermale 2.8659 0.3489 0.8590 9.562
ORdate_year 0.8955 1.1167 0.6283 1.276
Concordance= 0.679 (se = 0.06 )
Likelihood ratio test= 9.52 on 4 df, p=0.05
Wald test = 8.25 on 4 df, p=0.08
Score (logrank) test = 8.62 on 4 df, p=0.07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_rank
Effect size...............: -0.03367
Standard error............: 0.40417
Odds ratio (effect size)..: 0.967
Lower 95% CI..............: 0.438
Upper 95% CI..............: 2.135
T-value...................: -0.083308
P-value...................: 0.9336068
Sample size in model......: 549
Number of events..........: 26
> processing [MCP1_plasma_olink_rank]; 4 out of 4 proteins.
> cross tabulation of MCP1_plasma_olink_rank-stratum.
[-3.18339,0.00365) [ 0.00365,3.18339]
344 343
> fitting the model for MCP1_plasma_olink_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 687, number of events= 41
(1736 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 0.39977 1.49148 0.33404 1.197 0.2314
Age 0.03938 1.04016 0.01963 2.006 0.0448 *
Gendermale 0.41045 1.50750 0.37925 1.082 0.2791
ORdate_year -0.07220 0.93034 0.04054 -1.781 0.0749 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 1.4915 0.6705 0.7750 2.870
Age 1.0402 0.9614 1.0009 1.081
Gendermale 1.5075 0.6633 0.7169 3.170
ORdate_year 0.9303 1.0749 0.8593 1.007
Concordance= 0.644 (se = 0.043 )
Likelihood ratio test= 11.82 on 4 df, p=0.02
Wald test = 11.07 on 4 df, p=0.03
Score (logrank) test = 11.39 on 4 df, p=0.02
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_plasma_olink_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_plasma_olink_rank
Effect size...............: 0.399768
Standard error............: 0.334037
Odds ratio (effect size)..: 1.491
Lower 95% CI..............: 0.775
Upper 95% CI..............: 2.87
T-value...................: 1.196777
P-value...................: 0.2313933
Sample size in model......: 687
Number of events..........: 41 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}* Analyzing the effect of plaque proteins on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 4 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 115
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 1.541e-01 1.167e+00 1.963e-01 0.785 0.43241
Age 3.334e-02 1.034e+00 1.294e-02 2.577 0.00997 **
Gendermale 3.772e-01 1.458e+00 2.281e-01 1.654 0.09816 .
ORdate_year -1.256e-02 9.875e-01 3.425e-02 -0.367 0.71375
Hypertension.compositeno -4.222e-01 6.556e-01 3.564e-01 -1.185 0.23617
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -9.907e-03 9.901e-01 2.237e-01 -0.044 0.96468
SmokerStatusEx-smoker -4.967e-01 6.085e-01 2.095e-01 -2.371 0.01773 *
SmokerStatusNever smoked -8.052e-01 4.470e-01 3.417e-01 -2.356 0.01845 *
Med.Statin.LLDno 2.495e-01 1.283e+00 2.180e-01 1.144 0.25260
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.296e-01 1.537e+00 2.636e-01 1.630 0.10313
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.947e-02 9.807e-01 4.957e-03 -3.927 8.59e-05 ***
BMI 5.386e-02 1.055e+00 2.618e-02 2.057 0.03966 *
MedHx_CVDyes 5.346e-01 1.707e+00 2.220e-01 2.408 0.01603 *
stenose0-49% -1.565e+01 1.589e-07 2.454e+03 -0.006 0.99491
stenose50-70% -8.707e-01 4.186e-01 8.779e-01 -0.992 0.32127
stenose70-90% -3.015e-01 7.397e-01 7.471e-01 -0.404 0.68656
stenose90-99% -2.842e-01 7.526e-01 7.560e-01 -0.376 0.70696
stenose100% (Occlusion) -1.022e-01 9.028e-01 1.255e+00 -0.081 0.93505
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.530e+01 2.270e-07 2.929e+03 -0.005 0.99583
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 1.167e+00 8.572e-01 0.79401 1.7142
Age 1.034e+00 9.672e-01 1.00801 1.0605
Gendermale 1.458e+00 6.858e-01 0.93255 2.2803
ORdate_year 9.875e-01 1.013e+00 0.92340 1.0561
Hypertension.compositeno 6.556e-01 1.525e+00 0.32605 1.3183
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.901e-01 1.010e+00 0.63865 1.5351
SmokerStatusEx-smoker 6.085e-01 1.643e+00 0.40359 0.9175
SmokerStatusNever smoked 4.470e-01 2.237e+00 0.22880 0.8733
Med.Statin.LLDno 1.283e+00 7.792e-01 0.83702 1.9676
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.537e+00 6.508e-01 0.91667 2.5759
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.807e-01 1.020e+00 0.97124 0.9903
BMI 1.055e+00 9.476e-01 1.00255 1.1109
MedHx_CVDyes 1.707e+00 5.859e-01 1.10466 2.6373
stenose0-49% 1.589e-07 6.292e+06 0.00000 Inf
stenose50-70% 4.186e-01 2.389e+00 0.07492 2.3394
stenose70-90% 7.397e-01 1.352e+00 0.17107 3.1987
stenose90-99% 7.526e-01 1.329e+00 0.17103 3.3119
stenose100% (Occlusion) 9.028e-01 1.108e+00 0.07721 10.5562
stenoseNA NA NA NA NA
stenose50-99% 2.270e-07 4.406e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.698 (se = 0.023 )
Likelihood ratio test= 64.02 on 19 df, p=9e-07
Wald test = 59.22 on 19 df, p=5e-06
Score (logrank) test = 62.55 on 19 df, p=2e-06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.154129
Standard error............: 0.196327
Odds ratio (effect size)..: 1.167
Lower 95% CI..............: 0.794
Upper 95% CI..............: 1.714
T-value...................: 0.785066
P-value...................: 0.4324148
Sample size in model......: 1029
Number of events..........: 115
> processing [MCP1_pg_ml_2015_rank]; 2 out of 4 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 115
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.291e-01 1.138e+00 2.002e-01 0.645 0.518966
Age 3.304e-02 1.034e+00 1.293e-02 2.556 0.010591 *
Gendermale 3.709e-01 1.449e+00 2.288e-01 1.621 0.105078
ORdate_year -1.222e-02 9.879e-01 3.470e-02 -0.352 0.724616
Hypertension.compositeno -4.257e-01 6.533e-01 3.572e-01 -1.192 0.233306
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.766e-02 9.825e-01 2.237e-01 -0.079 0.937094
SmokerStatusEx-smoker -5.003e-01 6.063e-01 2.096e-01 -2.387 0.016973 *
SmokerStatusNever smoked -8.121e-01 4.439e-01 3.418e-01 -2.376 0.017500 *
Med.Statin.LLDno 2.512e-01 1.286e+00 2.183e-01 1.151 0.249766
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 4.271e-01 1.533e+00 2.637e-01 1.620 0.105327
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.926e-02 9.809e-01 4.962e-03 -3.880 0.000104 ***
BMI 5.407e-02 1.056e+00 2.610e-02 2.071 0.038324 *
MedHx_CVDyes 5.365e-01 1.710e+00 2.221e-01 2.416 0.015694 *
stenose0-49% -1.571e+01 1.504e-07 2.447e+03 -0.006 0.994877
stenose50-70% -8.674e-01 4.200e-01 8.780e-01 -0.988 0.323168
stenose70-90% -3.100e-01 7.334e-01 7.471e-01 -0.415 0.678201
stenose90-99% -2.933e-01 7.458e-01 7.560e-01 -0.388 0.698046
stenose100% (Occlusion) -1.521e-01 8.589e-01 1.253e+00 -0.121 0.903378
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.531e+01 2.252e-07 2.926e+03 -0.005 0.995826
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.138e+00 8.789e-01 0.76852 1.6846
Age 1.034e+00 9.675e-01 1.00773 1.0601
Gendermale 1.449e+00 6.901e-01 0.92531 2.2690
ORdate_year 9.879e-01 1.012e+00 0.92291 1.0574
Hypertension.compositeno 6.533e-01 1.531e+00 0.32438 1.3157
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.825e-01 1.018e+00 0.63374 1.5232
SmokerStatusEx-smoker 6.063e-01 1.649e+00 0.40210 0.9143
SmokerStatusNever smoked 4.439e-01 2.253e+00 0.22718 0.8674
Med.Statin.LLDno 1.286e+00 7.779e-01 0.83813 1.9719
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.533e+00 6.524e-01 0.91414 2.5702
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.809e-01 1.019e+00 0.97143 0.9905
BMI 1.056e+00 9.474e-01 1.00291 1.1110
MedHx_CVDyes 1.710e+00 5.848e-01 1.10656 2.6424
stenose0-49% 1.504e-07 6.648e+06 0.00000 Inf
stenose50-70% 4.200e-01 2.381e+00 0.07515 2.3477
stenose70-90% 7.334e-01 1.363e+00 0.16959 3.1720
stenose90-99% 7.458e-01 1.341e+00 0.16947 3.2821
stenose100% (Occlusion) 8.589e-01 1.164e+00 0.07373 10.0065
stenoseNA NA NA NA NA
stenose50-99% 2.252e-07 4.441e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.698 (se = 0.023 )
Likelihood ratio test= 63.82 on 19 df, p=9e-07
Wald test = 58.85 on 19 df, p=6e-06
Score (logrank) test = 62.26 on 19 df, p=2e-06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.129126
Standard error............: 0.200214
Odds ratio (effect size)..: 1.138
Lower 95% CI..............: 0.769
Upper 95% CI..............: 1.685
T-value...................: 0.64494
P-value...................: 0.5189661
Sample size in model......: 1029
Number of events..........: 115
> processing [MCP1_rank]; 3 out of 4 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 493, number of events= 61
(1930 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -3.920e-01 6.757e-01 2.685e-01 -1.460 0.1443
Age 3.117e-02 1.032e+00 1.813e-02 1.719 0.0857 .
Gendermale 8.127e-01 2.254e+00 3.652e-01 2.226 0.0260 *
ORdate_year -3.205e-02 9.685e-01 1.249e-01 -0.257 0.7975
Hypertension.compositeno -7.542e-01 4.704e-01 5.309e-01 -1.421 0.1555
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.309e-01 1.879e+00 2.951e-01 2.138 0.0325 *
SmokerStatusEx-smoker -6.391e-01 5.278e-01 2.880e-01 -2.219 0.0265 *
SmokerStatusNever smoked -3.224e-01 7.244e-01 4.307e-01 -0.748 0.4542
Med.Statin.LLDno 2.275e-01 1.255e+00 2.962e-01 0.768 0.4426
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -1.296e-02 9.871e-01 4.530e-01 -0.029 0.9772
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.091e-02 9.891e-01 6.796e-03 -1.606 0.1083
BMI 5.177e-03 1.005e+00 3.447e-02 0.150 0.8806
MedHx_CVDyes 5.574e-01 1.746e+00 3.075e-01 1.813 0.0699 .
stenose0-49% -1.651e+01 6.744e-08 3.444e+03 -0.005 0.9962
stenose50-70% -1.690e+00 1.845e-01 1.448e+00 -1.167 0.2431
stenose70-90% -7.523e-01 4.713e-01 1.049e+00 -0.717 0.4731
stenose90-99% -1.050e+00 3.501e-01 1.055e+00 -0.995 0.3198
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 6.757e-01 1.480e+00 0.39920 1.144
Age 1.032e+00 9.693e-01 0.99563 1.069
Gendermale 2.254e+00 4.436e-01 1.10189 4.611
ORdate_year 9.685e-01 1.033e+00 0.75814 1.237
Hypertension.compositeno 4.704e-01 2.126e+00 0.16617 1.332
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.879e+00 5.321e-01 1.05394 3.351
SmokerStatusEx-smoker 5.278e-01 1.895e+00 0.30015 0.928
SmokerStatusNever smoked 7.244e-01 1.380e+00 0.31142 1.685
Med.Statin.LLDno 1.255e+00 7.965e-01 0.70247 2.244
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 9.871e-01 1.013e+00 0.40623 2.399
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.891e-01 1.011e+00 0.97606 1.002
BMI 1.005e+00 9.948e-01 0.93952 1.075
MedHx_CVDyes 1.746e+00 5.727e-01 0.95573 3.190
stenose0-49% 6.744e-08 1.483e+07 0.00000 Inf
stenose50-70% 1.845e-01 5.420e+00 0.01080 3.151
stenose70-90% 4.713e-01 2.122e+00 0.06036 3.679
stenose90-99% 3.501e-01 2.857e+00 0.04426 2.768
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.703 (se = 0.03 )
Likelihood ratio test= 32.87 on 17 df, p=0.01
Wald test = 29.3 on 17 df, p=0.03
Score (logrank) test = 31.16 on 17 df, p=0.02
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_rank
Effect size...............: -0.392011
Standard error............: 0.268514
Odds ratio (effect size)..: 0.676
Lower 95% CI..............: 0.399
Upper 95% CI..............: 1.144
T-value...................: -1.459925
P-value...................: 0.1443106
Sample size in model......: 493
Number of events..........: 61
> processing [MCP1_plasma_olink_rank]; 4 out of 4 proteins.
> cross tabulation of MCP1_plasma_olink_rank-stratum.
[-3.18339,0.00365) [ 0.00365,3.18339]
344 343
> fitting the model for MCP1_plasma_olink_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 586, number of events= 107
(1837 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 4.555e-01 1.577e+00 2.222e-01 2.050 0.040379 *
Age 2.023e-02 1.020e+00 1.407e-02 1.438 0.150402
Gendermale 3.873e-01 1.473e+00 2.331e-01 1.662 0.096557 .
ORdate_year -3.093e-02 9.695e-01 2.649e-02 -1.168 0.242935
Hypertension.compositeno 5.456e-02 1.056e+00 2.902e-01 0.188 0.850886
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.562e-01 1.578e+00 2.232e-01 2.044 0.040949 *
SmokerStatusEx-smoker -2.980e-01 7.423e-01 2.239e-01 -1.331 0.183147
SmokerStatusNever smoked -2.965e-01 7.434e-01 3.343e-01 -0.887 0.375021
Med.Statin.LLDno 2.302e-01 1.259e+00 2.351e-01 0.979 0.327500
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 2.430e-02 1.025e+00 2.848e-01 0.085 0.932005
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.100e-02 9.891e-01 5.660e-03 -1.943 0.051966 .
BMI 5.363e-03 1.005e+00 2.647e-02 0.203 0.839474
MedHx_CVDyes 8.256e-01 2.283e+00 2.424e-01 3.406 0.000659 ***
stenose0-49% -1.429e+01 6.241e-07 2.217e+03 -0.006 0.994857
stenose50-70% 4.538e-01 1.574e+00 1.217e+00 0.373 0.709310
stenose70-90% 1.369e+00 3.932e+00 1.143e+00 1.198 0.230792
stenose90-99% 1.015e+00 2.760e+00 1.147e+00 0.885 0.375953
stenose100% (Occlusion) 2.731e+00 1.535e+01 1.511e+00 1.808 0.070682 .
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.430e+01 6.162e-07 2.997e+03 -0.005 0.996193
stenose70-99% 2.665e+00 1.437e+01 1.192e+00 2.236 0.025349 *
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 1.577e+00 6.341e-01 1.0202 2.438
Age 1.020e+00 9.800e-01 0.9927 1.049
Gendermale 1.473e+00 6.789e-01 0.9329 2.326
ORdate_year 9.695e-01 1.031e+00 0.9205 1.021
Hypertension.compositeno 1.056e+00 9.469e-01 0.5980 1.865
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.578e+00 6.337e-01 1.0189 2.444
SmokerStatusEx-smoker 7.423e-01 1.347e+00 0.4787 1.151
SmokerStatusNever smoked 7.434e-01 1.345e+00 0.3861 1.431
Med.Statin.LLDno 1.259e+00 7.944e-01 0.7940 1.996
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.025e+00 9.760e-01 0.5863 1.791
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.891e-01 1.011e+00 0.9782 1.000
BMI 1.005e+00 9.947e-01 0.9545 1.059
MedHx_CVDyes 2.283e+00 4.380e-01 1.4198 3.672
stenose0-49% 6.241e-07 1.602e+06 0.0000 Inf
stenose50-70% 1.574e+00 6.352e-01 0.1448 17.115
stenose70-90% 3.932e+00 2.543e-01 0.4188 36.916
stenose90-99% 2.760e+00 3.623e-01 0.2917 26.112
stenose100% (Occlusion) 1.535e+01 6.515e-02 0.7943 296.642
stenoseNA NA NA NA NA
stenose50-99% 6.162e-07 1.623e+06 0.0000 Inf
stenose70-99% 1.437e+01 6.959e-02 1.3896 148.576
stenose99 NA NA NA NA
Concordance= 0.722 (se = 0.023 )
Likelihood ratio test= 69.96 on 20 df, p=2e-07
Wald test = 65.48 on 20 df, p=1e-06
Score (logrank) test = 77.22 on 20 df, p=1e-08
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_plasma_olink_rank ' and its association to ' epmajor.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epmajor.3years
Protein...................: MCP1_plasma_olink_rank
Effect size...............: 0.455505
Standard error............: 0.222213
Odds ratio (effect size)..: 1.577
Lower 95% CI..............: 1.02
Upper 95% CI..............: 2.438
T-value...................: 2.049854
P-value...................: 0.04037869
Sample size in model......: 586
Number of events..........: 107
* Analyzing the effect of plaque proteins on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 4 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 59
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 2.608e-01 1.298e+00 2.783e-01 0.937 0.3487
Age 4.507e-02 1.046e+00 1.798e-02 2.507 0.0122 *
Gendermale -4.406e-02 9.569e-01 2.996e-01 -0.147 0.8831
ORdate_year -3.894e-02 9.618e-01 4.823e-02 -0.807 0.4194
Hypertension.compositeno 4.602e-04 1.000e+00 4.175e-01 0.001 0.9991
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -9.425e-03 9.906e-01 3.171e-01 -0.030 0.9763
SmokerStatusEx-smoker -1.164e-01 8.901e-01 2.964e-01 -0.393 0.6946
SmokerStatusNever smoked -9.547e-01 3.849e-01 5.245e-01 -1.820 0.0687 .
Med.Statin.LLDno 3.368e-01 1.400e+00 2.972e-01 1.133 0.2571
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.819e-01 1.465e+00 3.719e-01 1.027 0.3045
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.818e-03 9.962e-01 7.025e-03 -0.544 0.5868
BMI 8.126e-02 1.085e+00 3.469e-02 2.343 0.0191 *
MedHx_CVDyes 3.602e-01 1.434e+00 2.941e-01 1.225 0.2207
stenose0-49% -1.541e+01 2.036e-07 3.343e+03 -0.005 0.9963
stenose50-70% -6.563e-01 5.187e-01 1.173e+00 -0.559 0.5758
stenose70-90% -4.432e-01 6.420e-01 1.055e+00 -0.420 0.6744
stenose90-99% -4.878e-01 6.140e-01 1.069e+00 -0.456 0.6482
stenose100% (Occlusion) 4.139e-01 1.513e+00 1.460e+00 0.284 0.7768
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.517e+01 2.593e-07 3.993e+03 -0.004 0.9970
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 1.298e+00 7.704e-01 0.75225 2.240
Age 1.046e+00 9.559e-01 1.00988 1.084
Gendermale 9.569e-01 1.045e+00 0.53196 1.721
ORdate_year 9.618e-01 1.040e+00 0.87506 1.057
Hypertension.compositeno 1.000e+00 9.995e-01 0.44139 2.268
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.906e-01 1.009e+00 0.53206 1.844
SmokerStatusEx-smoker 8.901e-01 1.123e+00 0.49793 1.591
SmokerStatusNever smoked 3.849e-01 2.598e+00 0.13771 1.076
Med.Statin.LLDno 1.400e+00 7.140e-01 0.78215 2.508
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.465e+00 6.826e-01 0.70675 3.037
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.962e-01 1.004e+00 0.98257 1.010
BMI 1.085e+00 9.220e-01 1.01337 1.161
MedHx_CVDyes 1.434e+00 6.976e-01 0.80557 2.551
stenose0-49% 2.036e-07 4.911e+06 0.00000 Inf
stenose50-70% 5.187e-01 1.928e+00 0.05205 5.170
stenose70-90% 6.420e-01 1.558e+00 0.08124 5.074
stenose90-99% 6.140e-01 1.629e+00 0.07554 4.990
stenose100% (Occlusion) 1.513e+00 6.611e-01 0.08650 26.456
stenoseNA NA NA NA NA
stenose50-99% 2.593e-07 3.857e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.674 (se = 0.034 )
Likelihood ratio test= 23.74 on 19 df, p=0.2
Wald test = 21.67 on 19 df, p=0.3
Score (logrank) test = 22.9 on 19 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: 0.260803
Standard error............: 0.278315
Odds ratio (effect size)..: 1.298
Lower 95% CI..............: 0.752
Upper 95% CI..............: 2.24
T-value...................: 0.937078
P-value...................: 0.3487185
Sample size in model......: 1029
Number of events..........: 59
> processing [MCP1_pg_ml_2015_rank]; 2 out of 4 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 59
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.594e-01 1.173e+00 2.824e-01 0.564 0.5724
Age 4.416e-02 1.045e+00 1.793e-02 2.462 0.0138 *
Gendermale -4.998e-02 9.513e-01 3.010e-01 -0.166 0.8681
ORdate_year -3.475e-02 9.658e-01 4.903e-02 -0.709 0.4785
Hypertension.compositeno -1.230e-03 9.988e-01 4.192e-01 -0.003 0.9977
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -2.173e-02 9.785e-01 3.172e-01 -0.068 0.9454
SmokerStatusEx-smoker -1.136e-01 8.926e-01 2.965e-01 -0.383 0.7015
SmokerStatusNever smoked -9.518e-01 3.860e-01 5.240e-01 -1.817 0.0693 .
Med.Statin.LLDno 3.482e-01 1.417e+00 2.971e-01 1.172 0.2412
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.779e-01 1.459e+00 3.721e-01 1.016 0.3098
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.561e-03 9.964e-01 7.027e-03 -0.507 0.6124
BMI 8.022e-02 1.084e+00 3.436e-02 2.335 0.0195 *
MedHx_CVDyes 3.650e-01 1.441e+00 2.941e-01 1.241 0.2146
stenose0-49% -1.549e+01 1.867e-07 3.367e+03 -0.005 0.9963
stenose50-70% -6.477e-01 5.233e-01 1.173e+00 -0.552 0.5810
stenose70-90% -4.535e-01 6.354e-01 1.055e+00 -0.430 0.6673
stenose90-99% -5.009e-01 6.060e-01 1.070e+00 -0.468 0.6396
stenose100% (Occlusion) 3.518e-01 1.422e+00 1.459e+00 0.241 0.8094
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.518e+01 2.547e-07 3.975e+03 -0.004 0.9970
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.173e+00 8.526e-01 0.67425 2.040
Age 1.045e+00 9.568e-01 1.00905 1.083
Gendermale 9.513e-01 1.051e+00 0.52730 1.716
ORdate_year 9.658e-01 1.035e+00 0.87735 1.063
Hypertension.compositeno 9.988e-01 1.001e+00 0.43921 2.271
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.785e-01 1.022e+00 0.52549 1.822
SmokerStatusEx-smoker 8.926e-01 1.120e+00 0.49923 1.596
SmokerStatusNever smoked 3.860e-01 2.590e+00 0.13823 1.078
Med.Statin.LLDno 1.417e+00 7.059e-01 0.79124 2.536
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.459e+00 6.853e-01 0.70369 3.026
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.964e-01 1.004e+00 0.98282 1.010
BMI 1.084e+00 9.229e-01 1.01297 1.159
MedHx_CVDyes 1.441e+00 6.942e-01 0.80943 2.564
stenose0-49% 1.867e-07 5.356e+06 0.00000 Inf
stenose50-70% 5.233e-01 1.911e+00 0.05246 5.219
stenose70-90% 6.354e-01 1.574e+00 0.08037 5.024
stenose90-99% 6.060e-01 1.650e+00 0.07448 4.930
stenose100% (Occlusion) 1.422e+00 7.034e-01 0.08147 24.808
stenoseNA NA NA NA NA
stenose50-99% 2.547e-07 3.926e+06 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.672 (se = 0.034 )
Likelihood ratio test= 23.18 on 19 df, p=0.2
Wald test = 21.31 on 19 df, p=0.3
Score (logrank) test = 22.41 on 19 df, p=0.3
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.159428
Standard error............: 0.282444
Odds ratio (effect size)..: 1.173
Lower 95% CI..............: 0.674
Upper 95% CI..............: 2.04
T-value...................: 0.564459
P-value...................: 0.5724415
Sample size in model......: 1029
Number of events..........: 59
> processing [MCP1_rank]; 3 out of 4 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 493, number of events= 29
(1930 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -5.111e-01 5.998e-01 3.922e-01 -1.303 0.192
Age 1.682e-02 1.017e+00 2.487e-02 0.676 0.499
Gendermale 7.422e-02 1.077e+00 4.374e-01 0.170 0.865
ORdate_year -2.613e-02 9.742e-01 1.808e-01 -0.145 0.885
Hypertension.compositeno -8.185e-01 4.411e-01 7.527e-01 -1.087 0.277
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.130e-01 1.237e+00 4.580e-01 0.465 0.642
SmokerStatusEx-smoker -5.374e-01 5.843e-01 4.196e-01 -1.281 0.200
SmokerStatusNever smoked -3.304e-01 7.186e-01 6.147e-01 -0.537 0.591
Med.Statin.LLDno -6.489e-02 9.372e-01 4.561e-01 -0.142 0.887
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -8.493e-02 9.186e-01 6.709e-01 -0.127 0.899
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 2.245e-03 1.002e+00 1.037e-02 0.216 0.829
BMI -3.808e-03 9.962e-01 4.917e-02 -0.077 0.938
MedHx_CVDyes 2.618e-01 1.299e+00 4.162e-01 0.629 0.529
stenose0-49% -1.873e+01 7.320e-09 1.321e+04 -0.001 0.999
stenose50-70% -1.857e+01 8.632e-09 5.072e+03 -0.004 0.997
stenose70-90% -1.307e+00 2.705e-01 1.121e+00 -1.166 0.244
stenose90-99% -1.546e+00 2.130e-01 1.135e+00 -1.362 0.173
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 5.998e-01 1.667e+00 0.27811 1.294
Age 1.017e+00 9.833e-01 0.96858 1.068
Gendermale 1.077e+00 9.285e-01 0.45700 2.538
ORdate_year 9.742e-01 1.026e+00 0.68351 1.389
Hypertension.compositeno 4.411e-01 2.267e+00 0.10088 1.929
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.237e+00 8.081e-01 0.50429 3.036
SmokerStatusEx-smoker 5.843e-01 1.712e+00 0.25672 1.330
SmokerStatusNever smoked 7.186e-01 1.392e+00 0.21540 2.398
Med.Statin.LLDno 9.372e-01 1.067e+00 0.38338 2.291
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 9.186e-01 1.089e+00 0.24662 3.421
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.002e+00 9.978e-01 0.98209 1.023
BMI 9.962e-01 1.004e+00 0.90468 1.097
MedHx_CVDyes 1.299e+00 7.697e-01 0.57466 2.938
stenose0-49% 7.320e-09 1.366e+08 0.00000 Inf
stenose50-70% 8.632e-09 1.159e+08 0.00000 Inf
stenose70-90% 2.705e-01 3.697e+00 0.03004 2.436
stenose90-99% 2.130e-01 4.694e+00 0.02303 1.970
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.651 (se = 0.051 )
Likelihood ratio test= 9.82 on 17 df, p=0.9
Wald test = 7.29 on 17 df, p=1
Score (logrank) test = 9.15 on 17 df, p=0.9
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_rank
Effect size...............: -0.511114
Standard error............: 0.392158
Odds ratio (effect size)..: 0.6
Lower 95% CI..............: 0.278
Upper 95% CI..............: 1.294
T-value...................: -1.303338
P-value...................: 0.1924592
Sample size in model......: 493
Number of events..........: 29
> processing [MCP1_plasma_olink_rank]; 4 out of 4 proteins.
> cross tabulation of MCP1_plasma_olink_rank-stratum.
[-3.18339,0.00365) [ 0.00365,3.18339]
344 343
> fitting the model for MCP1_plasma_olink_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 586, number of events= 54
(1837 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 3.399e-01 1.405e+00 3.056e-01 1.112 0.26605
Age 5.058e-02 1.052e+00 1.962e-02 2.577 0.00996 **
Gendermale 3.637e-01 1.439e+00 3.379e-01 1.077 0.28169
ORdate_year -3.142e-02 9.691e-01 3.660e-02 -0.859 0.39059
Hypertension.compositeno 4.415e-01 1.555e+00 3.788e-01 1.165 0.24388
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.457e-01 1.278e+00 3.321e-01 0.740 0.45947
SmokerStatusEx-smoker 2.083e-01 1.232e+00 3.376e-01 0.617 0.53711
SmokerStatusNever smoked 2.481e-01 1.282e+00 4.844e-01 0.512 0.60860
Med.Statin.LLDno 5.333e-01 1.704e+00 3.183e-01 1.675 0.09392 .
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -1.245e-01 8.829e-01 4.169e-01 -0.299 0.76522
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 2.423e-03 1.002e+00 7.736e-03 0.313 0.75407
BMI 8.084e-02 1.084e+00 3.410e-02 2.370 0.01777 *
MedHx_CVDyes 6.578e-01 1.930e+00 3.322e-01 1.980 0.04772 *
stenose0-49% -1.609e+01 1.031e-07 4.393e+03 -0.004 0.99708
stenose50-70% -5.549e-01 5.741e-01 1.351e+00 -0.411 0.68123
stenose70-90% 6.273e-02 1.065e+00 1.248e+00 0.050 0.95990
stenose90-99% 1.065e-01 1.112e+00 1.251e+00 0.085 0.93212
stenose100% (Occlusion) 2.863e+00 1.751e+01 1.604e+00 1.785 0.07427 .
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.601e+01 1.114e-07 5.877e+03 -0.003 0.99783
stenose70-99% 1.437e+00 4.207e+00 1.338e+00 1.074 0.28291
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 1.405e+00 7.118e-01 0.77176 2.557
Age 1.052e+00 9.507e-01 1.01219 1.093
Gendermale 1.439e+00 6.951e-01 0.74193 2.790
ORdate_year 9.691e-01 1.032e+00 0.90198 1.041
Hypertension.compositeno 1.555e+00 6.431e-01 0.74005 3.267
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.278e+00 7.822e-01 0.66679 2.451
SmokerStatusEx-smoker 1.232e+00 8.119e-01 0.63555 2.387
SmokerStatusNever smoked 1.282e+00 7.803e-01 0.49589 3.312
Med.Statin.LLDno 1.704e+00 5.867e-01 0.91331 3.181
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 8.829e-01 1.133e+00 0.39004 1.999
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.002e+00 9.976e-01 0.98734 1.018
BMI 1.084e+00 9.223e-01 1.01409 1.159
MedHx_CVDyes 1.930e+00 5.180e-01 1.00664 3.702
stenose0-49% 1.031e-07 9.700e+06 0.00000 Inf
stenose50-70% 5.741e-01 1.742e+00 0.04067 8.106
stenose70-90% 1.065e+00 9.392e-01 0.09227 12.286
stenose90-99% 1.112e+00 8.989e-01 0.09585 12.911
stenose100% (Occlusion) 1.751e+01 5.712e-02 0.75530 405.738
stenoseNA NA NA NA NA
stenose50-99% 1.114e-07 8.975e+06 0.00000 Inf
stenose70-99% 4.207e+00 2.377e-01 0.30554 57.923
stenose99 NA NA NA NA
Concordance= 0.704 (se = 0.035 )
Likelihood ratio test= 37.92 on 20 df, p=0.009
Wald test = 31.45 on 20 df, p=0.05
Score (logrank) test = 47.88 on 20 df, p=4e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_plasma_olink_rank ' and its association to ' epstroke.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epstroke.3years
Protein...................: MCP1_plasma_olink_rank
Effect size...............: 0.339899
Standard error............: 0.30561
Odds ratio (effect size)..: 1.405
Lower 95% CI..............: 0.772
Upper 95% CI..............: 2.557
T-value...................: 1.112199
P-value...................: 0.2660526
Sample size in model......: 586
Number of events..........: 54
* Analyzing the effect of plaque proteins on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 4 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 78
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] -8.129e-02 9.219e-01 2.382e-01 -0.341 0.732935
Age 5.831e-04 1.001e+00 1.530e-02 0.038 0.969597
Gendermale 8.572e-01 2.357e+00 3.031e-01 2.828 0.004685 **
ORdate_year -2.828e-02 9.721e-01 4.226e-02 -0.669 0.503402
Hypertension.compositeno -9.168e-01 3.998e-01 5.209e-01 -1.760 0.078392 .
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -9.126e-02 9.128e-01 2.765e-01 -0.330 0.741338
SmokerStatusEx-smoker -6.172e-01 5.395e-01 2.576e-01 -2.396 0.016571 *
SmokerStatusNever smoked -2.571e-01 7.733e-01 3.650e-01 -0.704 0.481122
Med.Statin.LLDno 9.386e-02 1.098e+00 2.752e-01 0.341 0.733113
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.545e-01 1.425e+00 3.352e-01 1.058 0.290178
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.020e-02 9.800e-01 5.975e-03 -3.381 0.000723 ***
BMI 1.344e-02 1.014e+00 3.300e-02 0.407 0.683730
MedHx_CVDyes 6.888e-01 1.991e+00 2.795e-01 2.465 0.013715 *
stenose0-49% -1.597e+01 1.163e-07 3.040e+03 -0.005 0.995809
stenose50-70% -1.822e+00 1.617e-01 1.427e+00 -1.276 0.201823
stenose70-90% -2.739e-01 7.604e-01 1.044e+00 -0.262 0.792988
stenose90-99% -3.629e-01 6.956e-01 1.055e+00 -0.344 0.730864
stenose100% (Occlusion) -1.555e+01 1.767e-07 2.470e+03 -0.006 0.994977
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 8.026e-01 2.231e+00 1.430e+00 0.561 0.574547
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 9.219e-01 1.085e+00 0.57799 1.4705
Age 1.001e+00 9.994e-01 0.97103 1.0310
Gendermale 2.357e+00 4.244e-01 1.30095 4.2686
ORdate_year 9.721e-01 1.029e+00 0.89485 1.0561
Hypertension.compositeno 3.998e-01 2.501e+00 0.14403 1.1097
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.128e-01 1.096e+00 0.53092 1.5693
SmokerStatusEx-smoker 5.395e-01 1.854e+00 0.32561 0.8937
SmokerStatusNever smoked 7.733e-01 1.293e+00 0.37816 1.5812
Med.Statin.LLDno 1.098e+00 9.104e-01 0.64043 1.8839
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.425e+00 7.015e-01 0.73905 2.7495
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.800e-01 1.020e+00 0.96860 0.9915
BMI 1.014e+00 9.866e-01 0.95006 1.0812
MedHx_CVDyes 1.991e+00 5.022e-01 1.15147 3.4437
stenose0-49% 1.163e-07 8.601e+06 0.00000 Inf
stenose50-70% 1.617e-01 6.183e+00 0.00986 2.6530
stenose70-90% 7.604e-01 1.315e+00 0.09835 5.8798
stenose90-99% 6.956e-01 1.438e+00 0.08797 5.5012
stenose100% (Occlusion) 1.767e-07 5.658e+06 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 2.231e+00 4.482e-01 0.13540 36.7671
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.727 (se = 0.028 )
Likelihood ratio test= 50.41 on 19 df, p=1e-04
Wald test = 44.18 on 19 df, p=9e-04
Score (logrank) test = 47.82 on 19 df, p=3e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.081288
Standard error............: 0.238225
Odds ratio (effect size)..: 0.922
Lower 95% CI..............: 0.578
Upper 95% CI..............: 1.471
T-value...................: -0.341224
P-value...................: 0.7329348
Sample size in model......: 1029
Number of events..........: 78
> processing [MCP1_pg_ml_2015_rank]; 2 out of 4 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 78
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 2.905e-01 1.337e+00 2.410e-01 1.205 0.228034
Age 3.225e-04 1.000e+00 1.532e-02 0.021 0.983210
Gendermale 8.268e-01 2.286e+00 3.041e-01 2.719 0.006547 **
ORdate_year -4.520e-02 9.558e-01 4.217e-02 -1.072 0.283803
Hypertension.compositeno -9.704e-01 3.789e-01 5.215e-01 -1.861 0.062793 .
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -8.006e-02 9.231e-01 2.760e-01 -0.290 0.771732
SmokerStatusEx-smoker -6.194e-01 5.383e-01 2.585e-01 -2.396 0.016592 *
SmokerStatusNever smoked -2.735e-01 7.607e-01 3.654e-01 -0.748 0.454249
Med.Statin.LLDno 5.528e-02 1.057e+00 2.760e-01 0.200 0.841276
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 3.320e-01 1.394e+00 3.353e-01 0.990 0.322098
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.003e-02 9.802e-01 5.954e-03 -3.365 0.000766 ***
BMI 1.495e-02 1.015e+00 3.351e-02 0.446 0.655552
MedHx_CVDyes 6.941e-01 2.002e+00 2.795e-01 2.483 0.013015 *
stenose0-49% -1.602e+01 1.106e-07 3.018e+03 -0.005 0.995765
stenose50-70% -1.801e+00 1.651e-01 1.427e+00 -1.262 0.206807
stenose70-90% -2.542e-01 7.756e-01 1.043e+00 -0.244 0.807516
stenose90-99% -3.387e-01 7.127e-01 1.054e+00 -0.321 0.747924
stenose100% (Occlusion) -1.545e+01 1.953e-07 2.480e+03 -0.006 0.995030
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 7.799e-01 2.181e+00 1.430e+00 0.545 0.585612
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.337e+00 7.479e-01 0.83375 2.1442
Age 1.000e+00 9.997e-01 0.97072 1.0308
Gendermale 2.286e+00 4.374e-01 1.25965 4.1490
ORdate_year 9.558e-01 1.046e+00 0.87999 1.0382
Hypertension.compositeno 3.789e-01 2.639e+00 0.13634 1.0532
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.231e-01 1.083e+00 0.53742 1.5854
SmokerStatusEx-smoker 5.383e-01 1.858e+00 0.32429 0.8935
SmokerStatusNever smoked 7.607e-01 1.315e+00 0.37170 1.5570
Med.Statin.LLDno 1.057e+00 9.462e-01 0.61526 1.8153
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.394e+00 7.175e-01 0.72237 2.6894
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.802e-01 1.020e+00 0.96879 0.9917
BMI 1.015e+00 9.852e-01 0.95054 1.0840
MedHx_CVDyes 2.002e+00 4.995e-01 1.15752 3.4620
stenose0-49% 1.106e-07 9.039e+06 0.00000 Inf
stenose50-70% 1.651e-01 6.056e+00 0.01008 2.7052
stenose70-90% 7.756e-01 1.289e+00 0.10037 5.9929
stenose90-99% 7.127e-01 1.403e+00 0.09030 5.6244
stenose100% (Occlusion) 1.953e-07 5.121e+06 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 2.181e+00 4.584e-01 0.13215 36.0042
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.733 (se = 0.028 )
Likelihood ratio test= 51.75 on 19 df, p=7e-05
Wald test = 45.64 on 19 df, p=6e-04
Score (logrank) test = 49.2 on 19 df, p=2e-04
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.290473
Standard error............: 0.240969
Odds ratio (effect size)..: 1.337
Lower 95% CI..............: 0.834
Upper 95% CI..............: 2.144
T-value...................: 1.205438
P-value...................: 0.2280341
Sample size in model......: 1029
Number of events..........: 78
> processing [MCP1_rank]; 3 out of 4 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 493, number of events= 42
(1930 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 3.996e-02 1.041e+00 3.269e-01 0.122 0.9027
Age 4.142e-02 1.042e+00 2.293e-02 1.806 0.0709 .
Gendermale 9.841e-01 2.675e+00 4.684e-01 2.101 0.0356 *
ORdate_year -2.760e-01 7.588e-01 1.478e-01 -1.867 0.0620 .
Hypertension.compositeno -2.340e-01 7.914e-01 5.431e-01 -0.431 0.6666
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 5.455e-01 1.726e+00 3.576e-01 1.526 0.1271
SmokerStatusEx-smoker -4.047e-01 6.672e-01 3.492e-01 -1.159 0.2465
SmokerStatusNever smoked -1.727e-02 9.829e-01 5.071e-01 -0.034 0.9728
Med.Statin.LLDno -5.702e-02 9.446e-01 3.626e-01 -0.157 0.8751
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 6.030e-01 1.828e+00 4.594e-01 1.313 0.1893
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.491e-02 9.852e-01 8.623e-03 -1.730 0.0837 .
BMI 4.034e-02 1.041e+00 4.216e-02 0.957 0.3387
MedHx_CVDyes 1.532e-01 1.166e+00 3.489e-01 0.439 0.6605
stenose0-49% -1.499e-01 8.608e-01 8.796e+03 0.000 1.0000
stenose50-70% 1.616e+01 1.041e+07 5.185e+03 0.003 0.9975
stenose70-90% 1.636e+01 1.269e+07 5.185e+03 0.003 0.9975
stenose90-99% 1.621e+01 1.100e+07 5.185e+03 0.003 0.9975
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 1.041e+00 9.608e-01 0.5484 1.975
Age 1.042e+00 9.594e-01 0.9965 1.090
Gendermale 2.675e+00 3.738e-01 1.0683 6.700
ORdate_year 7.588e-01 1.318e+00 0.5679 1.014
Hypertension.compositeno 7.914e-01 1.264e+00 0.2729 2.295
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.726e+00 5.795e-01 0.8562 3.478
SmokerStatusEx-smoker 6.672e-01 1.499e+00 0.3365 1.323
SmokerStatusNever smoked 9.829e-01 1.017e+00 0.3638 2.655
Med.Statin.LLDno 9.446e-01 1.059e+00 0.4641 1.923
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.828e+00 5.472e-01 0.7427 4.497
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.852e-01 1.015e+00 0.9687 1.002
BMI 1.041e+00 9.605e-01 0.9586 1.131
MedHx_CVDyes 1.166e+00 8.580e-01 0.5883 2.309
stenose0-49% 8.608e-01 1.162e+00 0.0000 Inf
stenose50-70% 1.041e+07 9.610e-08 0.0000 Inf
stenose70-90% 1.269e+07 7.881e-08 0.0000 Inf
stenose90-99% 1.100e+07 9.094e-08 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.725 (se = 0.036 )
Likelihood ratio test= 25.04 on 17 df, p=0.09
Wald test = 15.97 on 17 df, p=0.5
Score (logrank) test = 24.24 on 17 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_rank
Effect size...............: 0.039956
Standard error............: 0.326862
Odds ratio (effect size)..: 1.041
Lower 95% CI..............: 0.548
Upper 95% CI..............: 1.975
T-value...................: 0.122242
P-value...................: 0.9027075
Sample size in model......: 493
Number of events..........: 42
> processing [MCP1_plasma_olink_rank]; 4 out of 4 proteins.
> cross tabulation of MCP1_plasma_olink_rank-stratum.
[-3.18339,0.00365) [ 0.00365,3.18339]
344 343
> fitting the model for MCP1_plasma_olink_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 586, number of events= 66
(1837 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 5.414e-01 1.718e+00 2.727e-01 1.985 0.047107 *
Age -2.839e-02 9.720e-01 1.805e-02 -1.573 0.115745
Gendermale 7.872e-01 2.197e+00 3.120e-01 2.523 0.011639 *
ORdate_year -2.773e-02 9.727e-01 3.356e-02 -0.826 0.408748
Hypertension.compositeno -5.819e-01 5.589e-01 4.156e-01 -1.400 0.161470
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.377e-01 1.892e+00 2.710e-01 2.353 0.018633 *
SmokerStatusEx-smoker -5.183e-01 5.955e-01 2.818e-01 -1.840 0.065834 .
SmokerStatusNever smoked 1.325e-02 1.013e+00 3.887e-01 0.034 0.972801
Med.Statin.LLDno -9.599e-02 9.085e-01 3.341e-01 -0.287 0.773894
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -6.763e-02 9.346e-01 3.871e-01 -0.175 0.861321
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.276e-02 9.873e-01 6.774e-03 -1.883 0.059635 .
BMI -4.701e-02 9.541e-01 3.560e-02 -1.320 0.186742
MedHx_CVDyes 1.128e+00 3.089e+00 3.254e-01 3.466 0.000528 ***
stenose0-49% 7.123e-01 2.039e+00 1.392e+04 0.000 0.999959
stenose50-70% 1.581e+01 7.319e+06 1.332e+04 0.001 0.999053
stenose70-90% 1.704e+01 2.514e+07 1.332e+04 0.001 0.998979
stenose90-99% 1.653e+01 1.507e+07 1.332e+04 0.001 0.999010
stenose100% (Occlusion) -5.477e-01 5.783e-01 1.553e+04 0.000 0.999972
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% 6.416e-01 1.900e+00 1.446e+04 0.000 0.999965
stenose70-99% 1.829e+01 8.763e+07 1.332e+04 0.001 0.998904
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 1.718e+00 5.819e-01 1.0069 2.933
Age 9.720e-01 1.029e+00 0.9382 1.007
Gendermale 2.197e+00 4.551e-01 1.1920 4.050
ORdate_year 9.727e-01 1.028e+00 0.9107 1.039
Hypertension.compositeno 5.589e-01 1.789e+00 0.2475 1.262
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.892e+00 5.285e-01 1.1124 3.219
SmokerStatusEx-smoker 5.955e-01 1.679e+00 0.3428 1.035
SmokerStatusNever smoked 1.013e+00 9.868e-01 0.4730 2.171
Med.Statin.LLDno 9.085e-01 1.101e+00 0.4719 1.749
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 9.346e-01 1.070e+00 0.4376 1.996
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.873e-01 1.013e+00 0.9743 1.001
BMI 9.541e-01 1.048e+00 0.8898 1.023
MedHx_CVDyes 3.089e+00 3.238e-01 1.6324 5.844
stenose0-49% 2.039e+00 4.905e-01 0.0000 Inf
stenose50-70% 7.319e+06 1.366e-07 0.0000 Inf
stenose70-90% 2.514e+07 3.977e-08 0.0000 Inf
stenose90-99% 1.507e+07 6.638e-08 0.0000 Inf
stenose100% (Occlusion) 5.783e-01 1.729e+00 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% 1.900e+00 5.264e-01 0.0000 Inf
stenose70-99% 8.763e+07 1.141e-08 0.0000 Inf
stenose99 NA NA NA NA
Concordance= 0.758 (se = 0.028 )
Likelihood ratio test= 58.4 on 20 df, p=1e-05
Wald test = 41.91 on 20 df, p=0.003
Score (logrank) test = 57.1 on 20 df, p=2e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_plasma_olink_rank ' and its association to ' epcoronary.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcoronary.3years
Protein...................: MCP1_plasma_olink_rank
Effect size...............: 0.541426
Standard error............: 0.272713
Odds ratio (effect size)..: 1.718
Lower 95% CI..............: 1.007
Upper 95% CI..............: 2.933
T-value...................: 1.985336
P-value...................: 0.04710706
Sample size in model......: 586
Number of events..........: 66
* Analyzing the effect of plaque proteins on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque proteins and start survival analysis.
> processing [MCP1_pg_ug_2015_rank]; 1 out of 4 proteins.
> cross tabulation of MCP1_pg_ug_2015_rank-stratum.
[-3.34102,0.00105) [ 0.00105,3.34102]
599 599
> fitting the model for MCP1_pg_ug_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 33
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] -1.032e-01 9.020e-01 3.706e-01 -0.278 0.780683
Age 7.038e-02 1.073e+00 2.717e-02 2.591 0.009583 **
Gendermale 1.235e+00 3.438e+00 5.583e-01 2.212 0.026994 *
ORdate_year -7.000e-02 9.324e-01 7.097e-02 -0.986 0.323965
Hypertension.compositeno -1.772e+01 2.022e-08 3.976e+03 -0.004 0.996445
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -1.617e-02 9.840e-01 4.283e-01 -0.038 0.969887
SmokerStatusEx-smoker -5.421e-01 5.815e-01 4.041e-01 -1.341 0.179820
SmokerStatusNever smoked -3.793e-01 6.844e-01 6.195e-01 -0.612 0.540357
Med.Statin.LLDno 2.953e-02 1.030e+00 4.214e-01 0.070 0.944129
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.119e+00 3.062e+00 4.176e-01 2.680 0.007360 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.306e-02 9.675e-01 9.439e-03 -3.502 0.000461 ***
BMI 8.403e-02 1.088e+00 5.163e-02 1.627 0.103639
MedHx_CVDyes 7.411e-01 2.098e+00 4.621e-01 1.604 0.108740
stenose0-49% -2.061e+01 1.123e-09 2.700e+04 -0.001 0.999391
stenose50-70% -1.280e+00 2.780e-01 1.264e+00 -1.013 0.311055
stenose70-90% -1.796e+00 1.659e-01 1.124e+00 -1.598 0.109956
stenose90-99% -1.508e+00 2.213e-01 1.152e+00 -1.310 0.190337
stenose100% (Occlusion) -1.994e+01 2.190e-09 1.979e+04 -0.001 0.999196
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.943e+01 3.651e-09 3.424e+04 -0.001 0.999547
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00105,3.34102] 9.020e-01 1.109e+00 0.43626 1.8648
Age 1.073e+00 9.320e-01 1.01728 1.1316
Gendermale 3.438e+00 2.909e-01 1.15085 10.2689
ORdate_year 9.324e-01 1.073e+00 0.81132 1.0715
Hypertension.compositeno 2.022e-08 4.946e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.840e-01 1.016e+00 0.42505 2.2778
SmokerStatusEx-smoker 5.815e-01 1.720e+00 0.26339 1.2840
SmokerStatusNever smoked 6.844e-01 1.461e+00 0.20323 2.3044
Med.Statin.LLDno 1.030e+00 9.709e-01 0.45098 2.3523
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 3.062e+00 3.265e-01 1.35084 6.9424
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.675e-01 1.034e+00 0.94974 0.9855
BMI 1.088e+00 9.194e-01 0.98298 1.2035
MedHx_CVDyes 2.098e+00 4.766e-01 0.84829 5.1897
stenose0-49% 1.123e-09 8.903e+08 0.00000 Inf
stenose50-70% 2.780e-01 3.597e+00 0.02336 3.3087
stenose70-90% 1.659e-01 6.027e+00 0.01834 1.5013
stenose90-99% 2.213e-01 4.519e+00 0.02315 2.1151
stenose100% (Occlusion) 2.190e-09 4.566e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 3.651e-09 2.739e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.843 (se = 0.031 )
Likelihood ratio test= 61.17 on 19 df, p=3e-06
Wald test = 22.08 on 19 df, p=0.3
Score (logrank) test = 57.15 on 19 df, p=1e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ug_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ug_2015_rank
Effect size...............: -0.103183
Standard error............: 0.37059
Odds ratio (effect size)..: 0.902
Lower 95% CI..............: 0.436
Upper 95% CI..............: 1.865
T-value...................: -0.278429
P-value...................: 0.7806834
Sample size in model......: 1029
Number of events..........: 33
> processing [MCP1_pg_ml_2015_rank]; 2 out of 4 proteins.
> cross tabulation of MCP1_pg_ml_2015_rank-stratum.
[-3.34125,0.00209) [ 0.00209,3.34125]
600 599
> fitting the model for MCP1_pg_ml_2015_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 1029, number of events= 33
(1394 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 3.757e-02 1.038e+00 3.794e-01 0.099 0.921123
Age 7.047e-02 1.073e+00 2.723e-02 2.588 0.009658 **
Gendermale 1.226e+00 3.407e+00 5.594e-01 2.191 0.028427 *
ORdate_year -7.706e-02 9.258e-01 7.153e-02 -1.077 0.281331
Hypertension.compositeno -1.773e+01 2.000e-08 3.957e+03 -0.004 0.996425
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes -9.565e-03 9.905e-01 4.279e-01 -0.022 0.982165
SmokerStatusEx-smoker -5.440e-01 5.804e-01 4.052e-01 -1.342 0.179449
SmokerStatusNever smoked -3.778e-01 6.854e-01 6.197e-01 -0.610 0.542134
Med.Statin.LLDno 1.675e-02 1.017e+00 4.225e-01 0.040 0.968375
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.115e+00 3.050e+00 4.178e-01 2.669 0.007602 **
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -3.284e-02 9.677e-01 9.422e-03 -3.485 0.000491 ***
BMI 8.583e-02 1.090e+00 5.240e-02 1.638 0.101466
MedHx_CVDyes 7.410e-01 2.098e+00 4.621e-01 1.603 0.108837
stenose0-49% -2.059e+01 1.144e-09 2.687e+04 -0.001 0.999389
stenose50-70% -1.271e+00 2.805e-01 1.263e+00 -1.007 0.314004
stenose70-90% -1.782e+00 1.683e-01 1.122e+00 -1.587 0.112409
stenose90-99% -1.497e+00 2.239e-01 1.150e+00 -1.301 0.193259
stenose100% (Occlusion) -1.989e+01 2.301e-09 1.983e+04 -0.001 0.999200
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -1.943e+01 3.629e-09 3.412e+04 -0.001 0.999546
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00209,3.34125] 1.038e+00 9.631e-01 0.49356 2.1842
Age 1.073e+00 9.320e-01 1.01724 1.1318
Gendermale 3.407e+00 2.935e-01 1.13818 10.1980
ORdate_year 9.258e-01 1.080e+00 0.80473 1.0652
Hypertension.compositeno 2.000e-08 5.000e+07 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 9.905e-01 1.010e+00 0.42820 2.2911
SmokerStatusEx-smoker 5.804e-01 1.723e+00 0.26230 1.2843
SmokerStatusNever smoked 6.854e-01 1.459e+00 0.20343 2.3092
Med.Statin.LLDno 1.017e+00 9.834e-01 0.44427 2.3276
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 3.050e+00 3.278e-01 1.34493 6.9181
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.677e-01 1.033e+00 0.94999 0.9857
BMI 1.090e+00 9.178e-01 0.98326 1.2075
MedHx_CVDyes 2.098e+00 4.766e-01 0.84810 5.1899
stenose0-49% 1.144e-09 8.743e+08 0.00000 Inf
stenose50-70% 2.805e-01 3.566e+00 0.02361 3.3316
stenose70-90% 1.683e-01 5.941e+00 0.01865 1.5191
stenose90-99% 2.239e-01 4.466e+00 0.02349 2.1340
stenose100% (Occlusion) 2.301e-09 4.346e+08 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% 3.629e-09 2.755e+08 0.00000 Inf
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.844 (se = 0.031 )
Likelihood ratio test= 61.1 on 19 df, p=3e-06
Wald test = 21.88 on 19 df, p=0.3
Score (logrank) test = 57.18 on 19 df, p=1e-05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_pg_ml_2015_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_pg_ml_2015_rank
Effect size...............: 0.037572
Standard error............: 0.37944
Odds ratio (effect size)..: 1.038
Lower 95% CI..............: 0.494
Upper 95% CI..............: 2.184
T-value...................: 0.099019
P-value...................: 0.921123
Sample size in model......: 1029
Number of events..........: 33
> processing [MCP1_rank]; 3 out of 4 proteins.
> cross tabulation of MCP1_rank-stratum.
[-3.12162,0.00225) [ 0.00225,3.12162]
278 278
> fitting the model for MCP1_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 493, number of events= 23
(1930 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] -2.418e-01 7.852e-01 4.414e-01 -0.548 0.5839
Age 5.035e-02 1.052e+00 3.206e-02 1.571 0.1163
Gendermale 1.075e+00 2.930e+00 6.727e-01 1.598 0.1100
ORdate_year -1.134e-01 8.928e-01 1.951e-01 -0.581 0.5610
Hypertension.compositeno -1.802e+01 1.487e-08 4.552e+03 -0.004 0.9968
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 5.064e-01 1.659e+00 5.312e-01 0.953 0.3405
SmokerStatusEx-smoker -6.006e-01 5.485e-01 4.733e-01 -1.269 0.2045
SmokerStatusNever smoked -1.010e-01 9.039e-01 7.296e-01 -0.138 0.8899
Med.Statin.LLDno 7.451e-01 2.107e+00 4.587e-01 1.625 0.1043
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 5.400e-01 1.716e+00 6.750e-01 0.800 0.4237
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.044e-02 9.798e-01 1.039e-02 -1.968 0.0491 *
BMI 2.176e-02 1.022e+00 5.934e-02 0.367 0.7139
MedHx_CVDyes 1.312e+00 3.713e+00 6.416e-01 2.044 0.0409 *
stenose0-49% -7.481e-01 4.733e-01 3.720e+04 0.000 1.0000
stenose50-70% 4.915e-01 1.635e+00 2.257e+04 0.000 1.0000
stenose70-90% 1.839e+01 9.706e+07 2.097e+04 0.001 0.9993
stenose90-99% 1.809e+01 7.220e+07 2.097e+04 0.001 0.9993
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00225,3.12162] 7.852e-01 1.274e+00 0.3306 1.8651
Age 1.052e+00 9.509e-01 0.9876 1.1199
Gendermale 2.930e+00 3.413e-01 0.7839 10.9510
ORdate_year 8.928e-01 1.120e+00 0.6091 1.3086
Hypertension.compositeno 1.487e-08 6.727e+07 0.0000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.659e+00 6.027e-01 0.5858 4.7001
SmokerStatusEx-smoker 5.485e-01 1.823e+00 0.2169 1.3869
SmokerStatusNever smoked 9.039e-01 1.106e+00 0.2163 3.7768
Med.Statin.LLDno 2.107e+00 4.747e-01 0.8574 5.1763
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.716e+00 5.827e-01 0.4571 6.4433
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.798e-01 1.021e+00 0.9600 0.9999
BMI 1.022e+00 9.785e-01 0.9098 1.1480
MedHx_CVDyes 3.713e+00 2.693e-01 1.0557 13.0574
stenose0-49% 4.733e-01 2.113e+00 0.0000 Inf
stenose50-70% 1.635e+00 6.117e-01 0.0000 Inf
stenose70-90% 9.706e+07 1.030e-08 0.0000 Inf
stenose90-99% 7.220e+07 1.385e-08 0.0000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.815 (se = 0.039 )
Likelihood ratio test= 33.08 on 17 df, p=0.01
Wald test = 12.37 on 17 df, p=0.8
Score (logrank) test = 27.71 on 17 df, p=0.05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_rank
Effect size...............: -0.241771
Standard error............: 0.441385
Odds ratio (effect size)..: 0.785
Lower 95% CI..............: 0.331
Upper 95% CI..............: 1.865
T-value...................: -0.547755
P-value...................: 0.5838601
Sample size in model......: 493
Number of events..........: 23
> processing [MCP1_plasma_olink_rank]; 4 out of 4 proteins.
> cross tabulation of MCP1_plasma_olink_rank-stratum.
[-3.18339,0.00365) [ 0.00365,3.18339]
344 343
> fitting the model for MCP1_plasma_olink_rank-stratum.
> make a Kaplan-Meier-shizzle...Vectorized input to `element_text()` is not officially supported.
Results may be unexpected or may change in future versions of ggplot2.
> perform the Cox-regression fashizzle and plot it...
Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 586, number of events= 33
(1837 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 2.073e-01 1.230e+00 3.944e-01 0.526 0.59909
Age 4.538e-02 1.046e+00 2.596e-02 1.748 0.08044 .
Gendermale 6.195e-01 1.858e+00 4.457e-01 1.390 0.16457
ORdate_year -1.206e-01 8.864e-01 5.337e-02 -2.260 0.02384 *
Hypertension.compositeno -7.253e-01 4.842e-01 6.335e-01 -1.145 0.25226
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.382e-01 1.550e+00 3.942e-01 1.112 0.26633
SmokerStatusEx-smoker -1.068e+00 3.436e-01 3.987e-01 -2.679 0.00738 **
SmokerStatusNever smoked -1.029e+00 3.573e-01 6.564e-01 -1.568 0.11695
Med.Statin.LLDno -2.285e-01 7.957e-01 4.468e-01 -0.511 0.60912
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 8.986e-01 2.456e+00 4.205e-01 2.137 0.03258 *
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.723e-02 9.829e-01 1.005e-02 -1.715 0.08635 .
BMI -2.760e-02 9.728e-01 5.146e-02 -0.536 0.59177
MedHx_CVDyes 1.326e+00 3.765e+00 5.420e-01 2.446 0.01444 *
stenose0-49% -4.133e-01 6.615e-01 2.255e+04 0.000 0.99999
stenose50-70% 1.458e+01 2.155e+06 2.179e+04 0.001 0.99947
stenose70-90% 1.552e+01 5.474e+06 2.179e+04 0.001 0.99943
stenose90-99% 1.520e+01 4.001e+06 2.179e+04 0.001 0.99944
stenose100% (Occlusion) -1.340e+00 2.619e-01 2.480e+04 0.000 0.99996
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% -7.838e-01 4.567e-01 2.368e+04 0.000 0.99997
stenose70-99% 1.679e+01 1.953e+07 2.179e+04 0.001 0.99939
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.PROTEIN.RANK[protein]]][ 0.00365,3.18339] 1.230e+00 8.128e-01 0.5680 2.6652
Age 1.046e+00 9.556e-01 0.9945 1.1011
Gendermale 1.858e+00 5.382e-01 0.7756 4.4509
ORdate_year 8.864e-01 1.128e+00 0.7983 0.9841
Hypertension.compositeno 4.842e-01 2.065e+00 0.1399 1.6759
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.550e+00 6.452e-01 0.7157 3.3560
SmokerStatusEx-smoker 3.436e-01 2.910e+00 0.1573 0.7507
SmokerStatusNever smoked 3.573e-01 2.799e+00 0.0987 1.2937
Med.Statin.LLDno 7.957e-01 1.257e+00 0.3315 1.9104
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.456e+00 4.071e-01 1.0774 5.5997
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.829e-01 1.017e+00 0.9638 1.0025
BMI 9.728e-01 1.028e+00 0.8794 1.0760
MedHx_CVDyes 3.765e+00 2.656e-01 1.3015 10.8923
stenose0-49% 6.615e-01 1.512e+00 0.0000 Inf
stenose50-70% 2.155e+06 4.640e-07 0.0000 Inf
stenose70-90% 5.474e+06 1.827e-07 0.0000 Inf
stenose90-99% 4.001e+06 2.499e-07 0.0000 Inf
stenose100% (Occlusion) 2.619e-01 3.818e+00 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% 4.567e-01 2.190e+00 0.0000 Inf
stenose70-99% 1.953e+07 5.121e-08 0.0000 Inf
stenose99 NA NA NA NA
Concordance= 0.807 (se = 0.034 )
Likelihood ratio test= 46.65 on 20 df, p=7e-04
Wald test = 29.44 on 20 df, p=0.08
Score (logrank) test = 44.39 on 20 df, p=0.001
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' MCP1_plasma_olink_rank ' and its association to ' epcvdeath.3years ' in ' AEDB.CEA '.
Collecting data.
We have collected the following:
Dataset used..............: AEDB.CEA
Outcome analyzed..........: epcvdeath.3years
Protein...................: MCP1_plasma_olink_rank
Effect size...............: 0.207324
Standard error............: 0.394367
Odds ratio (effect size)..: 1.23
Lower 95% CI..............: 0.568
Upper 95% CI..............: 2.665
T-value...................: 0.525714
P-value...................: 0.5990869
Sample size in model......: 586
Number of events..........: 33 
cat("- Edit the column names...\n")- Edit the column names...colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")- Correct the variable types...COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")- Writing results to Excel-file...head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")- Removing intermediate files...rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)object 'head.style' not found30-days follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
eptime = times30[i]
ep = endpoints30[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
eptime = times30[i]
ep = endpoints30[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)
90-days follow-up
MODEL 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
eptime = times90[i]
ep = endpoints90[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
#rm(head.style)
MODEL 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
eptime = times90[i]
ep = endpoints90[i]
cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AEDB.CEA)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)){
cat(paste0(" > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
ylim = c(0.75, 1),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.survival.",ep,".2G.",
TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
# ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [days]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AEDB.CEA.Cox.",ep,".2G.",
# Today,".AEDB.CEA.Cox.",ep,".4G.",
TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AEDB.CEA.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
row.names = FALSE, col.names = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)
rm(head.style)
Correlations
We correlated plasma and plaque levels of the biomarkers.
MCP1 plaque levels
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA,
# select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
# TRAITS.BIN, TRAITS.CON.RANK)
# )
# AEDB.CEA.temp <- subset(AEDB.CEA,
# select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
# TRAITS.BIN, TRAITS.CON.RANK)
# )
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("MCP1_pg_ug_2015_rank",
TRAITS.BIN,
TRAITS.CON.RANK,
"Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$MAC_binned <- as.numeric(AEDB.CEA.temp$MAC_binned)
AEDB.CEA.temp$SMC_binned <- as.numeric(AEDB.CEA.temp$SMC_binned)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
str(AEDB.CEA.temp)'data.frame': 2423 obs. of 14 variables:
$ MCP1_pg_ug_2015_rank: num 0.9383 1.9962 1.2595 -0.6407 -0.0387 ...
$ CalcificationPlaque : num 1 1 2 1 2 1 2 1 2 2 ...
$ CollagenPlaque : num 2 2 2 2 2 1 2 2 2 1 ...
$ Fat10Perc : num 2 2 1 2 2 2 1 2 2 2 ...
$ IPH : num 2 1 2 2 2 2 1 2 2 2 ...
$ MAC_binned : num 1 1 1 1 NA 1 1 2 2 1 ...
$ SMC_binned : num 1 2 2 2 1 2 2 2 2 1 ...
$ Macrophages_rank : num 1.121 0.396 0.29 0.32 -2.316 ...
$ SMC_rank : num 1.132 1.27 1.307 0.783 -0.828 ...
$ VesselDensity_rank : num -0.978 1.1 -0.858 -1.068 -0.231 ...
$ Symptoms.5G : num 5 6 5 2 6 6 2 6 6 5 ...
$ AsymptSympt : num 3 3 3 2 3 3 2 3 3 3 ...
$ EP_major : num 0 0 0 1 1 0 0 0 1 1 ...
$ EP_composite : num 2 2 2 3 3 2 2 2 3 3 ...AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
biomarker_row = rownames(cormat)[row(cormat)[ut]],
biomarker_column = rownames(cormat)[col(cormat)[ut]],
spearman_cor =(cormat)[ut],
pval = pmat[ut]
)
}
corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)
NA# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall",
"spearman"), ...)
{
x = checkData(R, method = "matrix")
if (missing(method))
method = method[1]
cormeth <- method
panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs",
method = cormeth, cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- cor(x, y, use = use, method = method)
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste(prefix, txt, sep = "")
if (missing(cex.cor))
cex <- 0.8/strwidth(txt)
test <- cor.test(as.numeric(x), as.numeric(y), method = method)
Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***",
"**", "*", ".", " "))
text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
text(0.8, 0.8, Signif, cex = cex, col = 2)
}
f <- function(t) {
dnorm(t, mean = mean(x), sd = sd.xts(x))
}
dotargs <- list(...)
dotargs$method <- NULL
rm(method)
hist.panel = function(x, ... = NULL) {
par(new = TRUE)
hist(x, col = "#1290D9", probability = TRUE, axes = FALSE,
# hist(x, col = "light gray", probability = TRUE, axes = FALSE,
main = "", breaks = "FD")
lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
rug(x)
}
if (histogram)
pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = hist.panel, ...)
else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}
chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
# ggpairs(AEDB.CEA,
# columns = c("MCP1_rank", "MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
# columnLabels = c("MCP1 (plasma)", "MCP1",
# "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
# method = c("spearman"),
# # ggplot2::aes(colour = Gender),
# progress = FALSE)
ggpairs(AEDB.CEA,
columns = c("MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK),
columnLabels = c("MCP1",
"Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Vessel density"),
method = c("spearman"),
# ggplot2::aes(colour = Gender),
progress = FALSE)Extra arguments: 'method' are being ignored. If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.
Circulating MCP1
Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:
- A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
- Associations of circulating MCP-1 levels with plaque vulnerability characteristics
- Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
- Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c("MCP1_plasma_olink_rank",
TRAITS.BIN,
TRAITS.CON.RANK,
"Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
)
AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$MAC_binned <- as.numeric(AEDB.CEA.temp$MAC_binned)
AEDB.CEA.temp$SMC_binned <- as.numeric(AEDB.CEA.temp$SMC_binned)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.plasma.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers_plasma.rank <- round(cor(AEDB.CEA.matrix.plasma.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
corr_biomarkers_plasma_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.plasma.RANK, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_plasma.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
# p.mat = corr_biomarkers_plasma_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
biomarker_row = rownames(cormat)[row(cormat)[ut]],
biomarker_column = rownames(cormat)[col(cormat)[ut]],
spearman_cor =(cormat)[ut],
pval = pmat[ut]
)
}
corr_biomarkers_plasma.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_plasma.rank, corr_biomarkers_plasma_p.rank))
DT::datatable(corr_biomarkers_plasma.rank.df)# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall",
"spearman"), ...)
{
x = checkData(R, method = "matrix")
if (missing(method))
method = method[1]
cormeth <- method
panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs",
method = cormeth, cex.cor, ...) {
usr <- par("usr")
on.exit(par(usr))
par(usr = c(0, 1, 0, 1))
r <- cor(x, y, use = use, method = method)
txt <- format(c(r, 0.123456789), digits = digits)[1]
txt <- paste(prefix, txt, sep = "")
if (missing(cex.cor))
cex <- 0.8/strwidth(txt)
test <- cor.test(as.numeric(x), as.numeric(y), method = method)
Signif <- symnum(test$p.value, corr = FALSE, na = FALSE,
cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***",
"**", "*", ".", " "))
text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
text(0.8, 0.8, Signif, cex = cex, col = 2)
}
f <- function(t) {
dnorm(t, mean = mean(x), sd = sd.xts(x))
}
dotargs <- list(...)
dotargs$method <- NULL
rm(method)
hist.panel = function(x, ... = NULL) {
par(new = TRUE)
hist(x, col = "#1290D9", probability = TRUE, axes = FALSE,
# hist(x, col = "light gray", probability = TRUE, axes = FALSE,
main = "", breaks = "FD")
lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
rug(x)
}
if (histogram)
pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor,
diag.panel = hist.panel, ...)
else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}
chart.Correlation.new(AEDB.CEA.matrix.plasma.RANK, method = "spearman", histogram = TRUE, pch = 3)
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")
# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables
library(GGally)
# From the help page:
ggpairs(AEDB.CEA,
columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
columnLabels = c("MCP1 (plasma)",
"Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Vessel density",
"Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
method = c("spearman"),
# ggplot2::aes(colour = Gender),
progress = FALSE) Extra arguments: 'method' are being ignored. If these are meant to be aesthetics, submit them using the 'mapping' variable within ggpairs with ggplot2::aes or ggplot2::aes_string.
Additional figures
Age and sex
We want to create per-age-group figures.
- Box and Whisker plot for MCP-1 plaque levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Box and Whisker plot for MCP-1 plasma levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Scatter plot of the correlation and regression line between MCP-1 levels in plaque (y axis) and plasma (x axis).
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
Age >= 55 & Age <= 64 ~ "55-64",
Age >= 65 & Age <= 74 ~ "65-74",
Age >= 75 & Age <= 84 ~ "75-84",
Age >= 85 ~ "85+")))
AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
Age >= 55 & Age <= 64 & Gender == "male"~ "55-64 males",
Age >= 65 & Age <= 74 & Gender == "male"~ "65-74 males",
Age >= 75 & Age <= 84 & Gender == "male"~ "75-84 males",
Age >= 85 & Gender == "male"~ "85+ males",
Age < 55 & Gender == "female" ~ "<55 females" ,
Age >= 55 & Age <= 64 & Gender == "female"~ "55-64 females ",
Age >= 65 & Age <= 74 & Gender == "female"~ "65-74 females",
Age >= 75 & Age <= 84 & Gender == "female"~ "75-84 females",
Age >= 85 & Gender == "female"~ "85+ females")))
table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
female male
<55 45 98
55-64 194 410
65-74 264 687
75-84 202 439
85+ 34 50table(AEDB.CEA$AgeGroupSex)
<55 females <55 males 55-64 females 55-64 males 65-74 females 65-74 males 75-84 females 75-84 males 85+ females 85+ males
45 98 194 410 264 687 202 439 34 50 Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
MCP1 plaque levels
# ?ggpubr::ggboxplot()
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Age groups (years)",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "AgeGroup",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AgeGroup.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AgeGroup_perGender.pdf"), plot = last_plot())MCP1 plasma levels
# compare_means(MCP1_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_plasma_olink_rank",
xlab = "Age groups (years)",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "AgeGroup",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.AgeGroup.pdf"), plot = last_plot())
# compare_means(MCP1_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_plasma_olink_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.AgeGroup_perGender.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# ?ggpubr::ggboxplot()
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Age groups (years)",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "AgeGroup",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AgeGroup.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AgeGroup_perGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
MCP1 plasma levels
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_plasma_olink",
xlab = "Age groups (years)",
ylab = "MCP1 plasma [AU]",
color = "AgeGroup",
palette = "npg",
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.AgeGroup.pdf"), plot = last_plot())
ggpubr::ggboxplot(AEDB.CEA,
x = c("AgeGroup"),
y = "MCP1_plasma_olink",
xlab = "Age groups (years) per gender",
ylab = "MCP1 plasma [AU]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.AgeGroup_perGender.pdf"), plot = last_plot())Hypertension & blood pressure
We want to create figures of MCP1 levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs.
- Box and Whisker plot for MCP-1 plaque levels by hypertension group (no, yes)
- Box and Whisker plot for MCP-1 plasma levels by systolic blood pressure group (<120, 120-139, 140-159,160+)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
systolic >= 120 & systolic <= 139 ~ "120-139",
systolic >= 140 & systolic <= 159 ~ "140-159",
systolic >= 160 ~ "160+")))
table(AEDB.CEA$SBPGroup, AEDB.CEA$Gender)
female male
<120 54 114
120-139 145 326
140-159 197 497
160+ 269 548Now we can draw some graphs of plasma/plaque MCP1 levels per sex and hypertension/blood pressure group.
Inverse-rank transformed data
MCP1 plaque levels
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.SBPGroup.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Self-reported hypertension per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Self-reported hypertension per medication use",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.HypertensionDrugs.pdf"), plot = last_plot())
MCP1 plasma levels
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "MCP1_plasma_olink_rank",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.SBPGroup.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "MCP1_plasma_olink_rank",
xlab = "Self-reported hypertension per gender",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Hypertension.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "MCP1_plasma_olink_rank",
xlab = "Self-reported hypertension per medication use",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.HypertensionDrugs.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.SBPGroup.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Self-reported hypertension per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Self-reported hypertension per medication use",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.HypertensionDrugs.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
MCP1 plasma levels
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)),
x = c("SBPGroup"),
y = "MCP1_plasma_olink",
xlab = "Systolic blood pressure (mmHg) per gender",
ylab = "MCP1 plasma [AU]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.SBPGroup.pdf"), plot = last_plot())
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)),
x = c("Hypertension.selfreport"),
y = "MCP1_plasma_olink",
xlab = "Self-reported hypertension per gender",
ylab = "MCP1 plasma [AU]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Hypertension.pdf"), plot = last_plot())
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)),
x = c("Hypertension.selfreport"),
y = "MCP1_plasma_olink",
xlab = "Self-reported hypertension per medication use",
ylab = "MCP1 plasma [AU]",
color = "Hypertension.drugs",
palette = c("#49A01D", "#1290D9"),
add = "jitter")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.HypertensionDrugs.pdf"), plot = last_plot())
Hypercholesterolemia & LDL levels
We want to create figures of MCP1 levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs.
- Box and Whisker plot for MCP-1 plaque levels by hypercholesterolemia group (no, yes)
- Box and Whisker plot for MCP-1 plasma levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
LDL_finalCU >= 100 & LDL_finalCU <= 129 ~ "100-129",
LDL_finalCU >= 130 & LDL_finalCU <= 159 ~ "130-159",
LDL_finalCU >= 160 & LDL_finalCU <= 189 ~ "160-189",
LDL_finalCU >= 190 ~ "190+")))
table(AEDB.CEA$LDLGroup, AEDB.CEA$Gender)
female male
<100 171 441
100-129 96 250
130-159 75 129
160-189 40 50
190+ 25 31Now we can draw some graphs of plasma/plaque MCP1 levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users.
Inverse-rank transformed data
MCP1 plaque levels
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "LDL (mg/dL) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaqua.LDLGroups.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "LDL (mg/dL) per LLD use",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypercholesterolemia.pdf"), plot = last_plot())
MCP1 plasma levels
# compare_means(MCP1_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "MCP1_plasma_olink_rank",
xlab = "LDL (mg/dL) per gender",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.LDLGroup.pdf"), plot = last_plot())
# compare_means(MCP1_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "MCP1_plasma_olink_rank",
xlab = "LDL (mg/dL) per LLD use",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Hypercholesterolemia.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "LDL (mg/dL) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaqua.LDLGroups.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "LDL (mg/dL) per LLD use",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypercholesterolemia.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
MCP1 plasma levels
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)),
x = c("LDLGroup"),
y = "MCP1",
xlab = "LDL (mg/dL) per gender",
ylab = "MCP1 plasma [pg/mL]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.LDLGroup.pdf"), plot = last_plot())
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
x = c("LDLGroup"),
y = "MCP1",
xlab = "LDL (mg/dL) per LLD use",
ylab = "MCP1 plasma [pg/mL]",
color = "Med.Statin.LLD",
palette = c("#49A01D", "#1290D9"),
# add = "median_iqr")
add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Hypercholesterolemia.pdf"), plot = last_plot())
Kidney function (eGFR)
We want to create figures of MCP1 levels stratified by kidney function.
- Box and Whisker plot for MCP-1 plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for MCP-1 plasma levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
GFR_MDRD >= 15 & GFR_MDRD <= 29 ~ "15-29",
GFR_MDRD >= 30 & GFR_MDRD <= 59 ~ "30-59",
GFR_MDRD >= 60 & GFR_MDRD <= 89 ~ "60-89",
GFR_MDRD >= 90 ~ "90+")))
table(AEDB.CEA$eGFRGroup, AEDB.CEA$Gender)
female male
<15 3 7
15-29 7 20
30-59 193 325
60-89 361 845
90+ 117 345table(AEDB.CEA$eGFRGroup, AEDB.CEA$KDOQI)
No data available/missing Normal kidney function CKD 2 (Mild) CKD 3 (Moderate) CKD 4 (Severe) CKD 5 (Failure)
<15 0 0 0 0 0 10
15-29 0 0 0 0 27 0
30-59 0 0 0 518 0 0
60-89 0 0 1206 0 0 0
90+ 0 462 0 0 0 0Now we can draw some graphs of plasma/plaque MCP1 levels per sex and kidney function group.
Inverse-rank transformed data
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Kidney function (KDOQI) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.KDOQI.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "KDOQI",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR_KDOQI.pdf"), plot = last_plot())
MCP1 plasma levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "MCP1_plasma_olink_rank",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.EGFR.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "MCP1_plasma_olink_rank",
xlab = "Kidney function (KDOQI) per gender",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.KDOQI.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "MCP1_plasma_olink_rank",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "KDOQI",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.EGFR_KDOQI.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Kidney function (KDOQI) per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.KDOQI.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "KDOQI",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR_KDOQI.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
MCP1 plasma levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)),
x = c("eGFRGroup"),
y = "MCP1_plasma_olink",
xlab = "eGFR (mL/min per 1.73 m2) per gender",
ylab = "MCP1 plasma [AU]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.EGFR.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)),
x = c("KDOQI"),
y = "MCP1_plasma_olink",
xlab = "Kidney function (KDOQI) per gender",
ylab = "MCP1 plasma [AU]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.KDOQI.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)),
x = c("eGFRGroup"),
y = "MCP1_plasma_olink",
xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
ylab = "MCP1 plasma [AU]",
color = "KDOQI",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.EGFR_KDOQI.pdf"), plot = last_plot())
BMI
We want to create figures of MCP1 levels stratified by BMI.
- Box and Whisker plot for MCP-1 plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for MCP-1 plasma levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
BMI >= 18.5 & BMI < 25 ~ "18.5-24",
BMI >= 25 & BMI < 30 ~ "25-29",
BMI >= 30 & BMI < 35 ~ "30-35",
BMI >= 35 ~ "35+")))
# require(labelled)
# AEDB.CEA$BMI_US <- as_factor(AEDB.CEA$BMI_US)
# AEDB.CEA$BMI_WHO <- as_factor(AEDB.CEA$BMI_WHO)
# table(AEDB.CEA$BMI_WHO, AEDB.CEA$BMI_US)
table(AEDB.CEA$BMIGroup, AEDB.CEA$Gender)
female male
<18.5 17 8
18.5-24 277 574
25-29 267 786
30-35 99 189
35+ 32 32table(AEDB.CEA$BMIGroup, AEDB.CEA$BMI_WHO)
No data available/missing Underweight Normal Overweight Obese
<18.5 0 24 0 0 0
18.5-24 0 0 851 0 0
25-29 0 0 0 1052 0
30-35 0 0 0 0 288
35+ 0 0 0 0 64Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI_byGender.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "BMI_WHO",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI_byWHO.pdf"), plot = last_plot())
MCP1 plasma levels
NOT AVAILABLE YET
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_plasma_olink_rank",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.BMI.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_plasma_olink_rank",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.BMI_byGender.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("AgeGroup"),
y = "MCP1_plasma_olink_rank",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "BMI_WHO",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.BMI_byWHO.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("AgeGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "BMI_WHO",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI_byWHO.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
MCP1 plasma levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_plasma_olink",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plasma [AU]",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.BMI.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)),
x = c("BMIGroup"),
y = "MCP1_plasma_olink",
xlab = "BMI groups (kg/m2)",
ylab = "MCP1 plasma [AU]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.BMI_byGender.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)),
x = c("AgeGroup"),
y = "MCP1_plasma_olink",
xlab = "BMI groups (kg/m2) per WHO categories",
ylab = "MCP1 plasma [AU]",
color = "BMI_WHO",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.BMI_byWHO.pdf"), plot = last_plot())
Diabetes
We want to create figures of MCP1 levels stratified by type 2 diabetes.
- Box and Whisker plot for MCP-1 plaque levels by type 2 diabetes group (no, yes)
Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Diabetes status",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Diabetes.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Diabetes status per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Diabetes_byGender.pdf"), plot = last_plot())
MCP1 plasma levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_plasma_olink_rank",
xlab = "Diabetes status",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Diabetes.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_plasma_olink_rank",
xlab = "Diabetes status per gender",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Diabetes_byGender.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Diabetes status",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Diabetes.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Diabetes status per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Diabetes_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
MCP1 plasma levels
# Global test
# compare_means(MCP1_pg_ug_2015 ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_plasma_olink",
xlab = "Diabetes status",
ylab = "MCP1 plasma [AU]",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = c("median_iqr", "jitter")) #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.rawDiabetes.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015 ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)),
x = c("DiabetesStatus"),
y = "MCP1_plasma_olink",
xlab = "Diabetes status per gender",
ylab = "MCP1 plasma [AU]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = c("median_iqr", "jitter")) #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Diabetes_byGender.pdf"), plot = last_plot())
Smoking
We want to create figures of MCP1 levels stratified by smoking.
- Box and Whisker plot for MCP-1 plaque levels by smoking group (never, ex, current)
Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Smoker status",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Smoking.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Smoker status per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Smoking_byGender.pdf"), plot = last_plot())
MCP1 plasma levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_plasma_olink_rank",
xlab = "Smoker status",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Smoking.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_plasma_olink_rank",
xlab = "Smoker status per gender",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Smoking_byGender.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Smoker status",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Smoking.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Smoker status per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Smoking_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
MCP1 plasma levels
# Global test
# compare_means(MCP1_pg_ug_2015 ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_plasma_olink",
xlab = "Smoker status",
ylab = "MCP1 plasma [AU]",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = c("median_iqr", "jitter")) #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Smoking.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015 ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)),
x = c("SmokerStatus"),
y = "MCP1_plasma_olink",
xlab = "Smoker status per gender",
ylab = "MCP1 plasma [AU]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = c("median_iqr", "jitter")) #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Smoking_byGender.pdf"), plot = last_plot())
Stenosis
We want to create figures of MCP1 levels stratified by stenosis grade.
- Box and Whisker plot for MCP-1 plaque levels by stenosis grade group (<70, 70-89, 90+)
library(dplyr)
AEDB.CEA <- AEDB.CEA %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
stenose == "0-49%" ~ "<70",
stenose == "50-70%" ~ "<70",
stenose == "70-90%" ~ "70-89",
stenose == "50-99%" ~ "90+",
stenose == "70-99%" ~ "90+",
stenose == "100% (Occlusion)" ~ "90+",
stenose == "90-99%" ~ "90+")))
table(AEDB.CEA$StenoticGroup, AEDB.CEA$Gender)
female male
<70 46 157
70-89 365 762
90+ 316 726table(AEDB.CEA$stenose, AEDB.CEA$StenoticGroup)
<70 70-89 90+
missing 0 0 0
0-49% 13 0 0
50-70% 190 0 0
70-90% 0 1127 0
90-99% 0 0 928
100% (Occlusion) 0 0 31
NA 0 0 0
50-99% 0 0 15
70-99% 0 0 68
99 0 0 0Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.
Inverse-rank transformed data
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Stenotic grade",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Stenosis.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Stenotic grade per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Stenosis_byGender.pdf"), plot = last_plot())
MCP1 plasma levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_plasma_olink_rank",
xlab = "Stenotic grade",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Stenosis.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_plasma_olink_rank",
xlab = "Stenotic grade per gender",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Stenosis_byGender.pdf"), plot = last_plot())
Raw data
Simalarly but now for the raw data as median ± interquartile range.
MCP1 plaque levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Stenotic grade",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Stenosis.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_pg_ug_2015_rank",
xlab = "Stenotic grade per gender",
ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Stenosis_byGender.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
MCP1 plasma levels
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_plasma_olink",
xlab = "Stenotic grade",
ylab = "MCP1 plasma [AU]",
# color = "Gender",
# palette = c("#D5267B", "#1290D9"),
color = "#1290D9",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Stenosis.pdf"), plot = last_plot())
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)),
x = c("StenoticGroup"),
y = "MCP1_plasma_olink",
xlab = "Stenotic grade per gender",
ylab = "MCP1 plasma [AU]",
color = "Gender",
palette = c("#D5267B", "#1290D9"),
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Stenosis_byGender.pdf"), plot = last_plot())
Circulating vs. plaque MCP1 levels
We will also make a nice correlation plot between plasma and plaque MCP1 levels.
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_pg_ug_2015",
y = "MCP1_plasma_olink",
xlab = "MCP1 plaque [pg/ug]",
ylab = "MCP1 plasma [AU]",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque_vs_plasma.raw.pdf"), plot = last_plot())
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_pg_ug_2015_rank",
y = "MCP1_plasma_olink_rank",
xlab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque_vs_plasma.rank.pdf"), plot = last_plot())
Plaque vs. plaque MCP1 levels
We will also make a nice correlation plot between the two experiments of plaque MCP1 levels.
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
summary(AEDB.CEA$MCP1)
summary(AEDB.CEA$MCP1_pg_ug_2015)
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1",
y = "MCP1_pg_ug_2015",
xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
ylab = "MCP1 plaque [pg/ug] (exp. no. 2)",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque_vs_plaque.raw.pdf"), plot = last_plot())
ggpubr::ggscatter(AEDB.CEA,
x = "MCP1_rank",
y = "MCP1_pg_ug_2015_rank",
xlab = "MCP1 plaque [pg/mL]\n(INRT, exp. no. 1)",
ylab = "MCP1 plaque [pg/ug]\n(INRT, exp. no. 2)",
add = "reg.line", add.params = list(color = "#1290D9"),
conf.int = TRUE,
cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque_vs_plasma.rank.pdf"), plot = last_plot())
Symptoms
We want to create per-symptom figures.
library(dplyr)
table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
<55 24 119
55-64 76 528
65-74 124 827
75-84 43 598
85+ 3 81table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
female 64 675
male 206 1478table(AEDB.CEA$AsymptSympt2G)
Asymptomatic Symptomatic
270 2153 Now we can draw some graphs of plasma/plaque MCP1 levels per symptom group.
# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "AsymptSympt2G", y = "MCP1_pg_ug_2015_rank",
title = "MCP1 plaque [pg/ug] levels per symptom",
xlab = "Symptoms",
ylab = "MCP1 plaque [pg/ug]\n inverse-rank transformation",
color = "AsymptSympt2G",
palette = c(uithof_color[16], uithof_color[23]),
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AsymptSympt2G.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(p1)p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Symptoms.5G", y = "MCP1_plasma_olink_rank",
title = "MCP1 plasma [AU] levels per symptom",
xlab = "Symptoms",
ylab = "MCP1 plasma [AU]\n inverse-rank transformation",
color = "Symptoms.5G",
palette = "npg",
add = "dotplot", # Add dotplot
add.params = list(binwidth = 0.1, dotsize = 0.3)
) +
stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1 + rotate_x_text(45), legend = c("right"), legend.title = "Symptoms")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.AsymptSympt2G.pdf"), plot = last_plot())
rm(p1)
Forest plots
We would also like to visualize the multivariable analyses results.
library(ggplot2)
library(openxlsx)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"
models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1NAForest plot for experiment 2.
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"),
levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]))
fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) +
geom_hline(yintercept = 1, lty = 2) + # add a dotted line at x=1 after flip
coord_flip(ylim = c(0.8, 1.7)) + # flip coordinates (puts labels on y axis)
xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
ggtitle("Plaque MCP-1 levels (1 SD increment, exp. #2, n = 1190+)") +
theme_minimal() # use a white background
print(fp)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.exp2.forest.pdf"), plot = fp)
rm(fp)Forest plot for experiment 1.
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"),
levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))
fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) +
geom_hline(yintercept = 1, lty = 2) + # add a dotted line at x=1 after flip
coord_flip(ylim = c(0.8, 1.7)) + # flip coordinates (puts labels on y axis)
xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
ggtitle("Plaque MCP-1 levels (1 SD increment, exp. #1, n = 490+)") +
theme_minimal() # use a white background
print(fp)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.exp1.forest.pdf"), plot = fp)
rm(fp)MCP1 vs. cytokines plaque levels correlations
We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:
- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB
In addition we will look at three metalloproteinases which were measured using an activity assay.
- MMP2
- MMP8
- MMP9
The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the MCP1 plaque levels.
We will set the measurements that yielded ‘0’ to NA, as it is unlikely that any protein ever has exactly 0 copies. The ‘0’ yielded during the experiment are due to the limits of the detection.
Prepare data
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21",
"INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1",
"TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")
# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"
proteins_of_interest <- c(cytokines, metalloproteinases)
proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))
# make variables numerics()
AEDB.CEA <- AEDB.CEA %>%
mutate_each(funs(as.numeric), proteins_of_interest)
for(PROTEIN in 1:length(proteins_of_interest)){
# UCORBIOGSAqc$Z <- NULL
var.temp.rank = proteins_of_interest_rank[PROTEIN]
var.temp = proteins_of_interest[PROTEIN]
cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
cat(paste0("* changing ", var.temp, " to numeric.\n"))
# AEDB.CEA <- AEDB.CEA %>% mutate(AEDB.CEA[,var.temp] == replace(AEDB.CEA[,var.temp], AEDB.CEA[,var.temp]==0, NA))
AEDB.CEA[,var.temp][AEDB.CEA[,var.temp]==0.000000]=NA
cat(paste0("* standardising ", var.temp,
" (mean: ",round(mean(!is.na(AEDB.CEA[,var.temp])), digits = 6),
", n = ",sum(!is.na(AEDB.CEA[,var.temp])),").\n"))
AEDB.CEA <- AEDB.CEA %>%
mutate_at(vars(var.temp),
# list(Z = ~ (AEDB.CEA[,var.temp] - mean(AEDB.CEA[,var.temp], na.rm = TRUE))/sd(AEDB.CEA[,var.temp], na.rm = TRUE))
list(RANK = ~ qnorm((rank(AEDB.CEA[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA[,var.temp]))))
)
# str(UCORBIOGSAqc$Z)
cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
AEDB.CEA[,var.temp.rank] <- NULL
names(AEDB.CEA)[names(AEDB.CEA) == "RANK"] <- var.temp.rank
}
Selecting IL2 and standardising: IL2_rank.
* changing IL2 to numeric.
* standardising IL2 (mean: 0.179942, n = 436).
* renaming RANK to IL2_rank.
Selecting IL4 and standardising: IL4_rank.
* changing IL4 to numeric.
* standardising IL4 (mean: 0.167561, n = 406).
* renaming RANK to IL4_rank.
Selecting IL5 and standardising: IL5_rank.
* changing IL5 to numeric.
* standardising IL5 (mean: 0.178291, n = 432).
* renaming RANK to IL5_rank.
Selecting IL6 and standardising: IL6_rank.
* changing IL6 to numeric.
* standardising IL6 (mean: 0.188196, n = 456).
* renaming RANK to IL6_rank.
Selecting IL8 and standardising: IL8_rank.
* changing IL8 to numeric.
* standardising IL8 (mean: 0.182006, n = 441).
* renaming RANK to IL8_rank.
Selecting IL9 and standardising: IL9_rank.
* changing IL9 to numeric.
* standardising IL9 (mean: 0.206356, n = 500).
* renaming RANK to IL9_rank.
Selecting IL10 and standardising: IL10_rank.
* changing IL10 to numeric.
* standardising IL10 (mean: 0.158894, n = 385).
* renaming RANK to IL10_rank.
Selecting IL12 and standardising: IL12_rank.
* changing IL12 to numeric.
* standardising IL12 (mean: 0.167974, n = 407).
* renaming RANK to IL12_rank.
Selecting IL13 and standardising: IL13_rank.
* changing IL13 to numeric.
* standardising IL13 (mean: 0.232769, n = 564).
* renaming RANK to IL13_rank.
Selecting IL21 and standardising: IL21_rank.
* changing IL21 to numeric.
* standardising IL21 (mean: 0.233182, n = 565).
* renaming RANK to IL21_rank.
Selecting INFG and standardising: INFG_rank.
* changing INFG to numeric.
* standardising INFG (mean: 0.179117, n = 434).
* renaming RANK to INFG_rank.
Selecting TNFA and standardising: TNFA_rank.
* changing TNFA to numeric.
* standardising TNFA (mean: 0.163434, n = 396).
* renaming RANK to TNFA_rank.
Selecting MIF and standardising: MIF_rank.
* changing MIF to numeric.
* standardising MIF (mean: 0.233182, n = 565).
* renaming RANK to MIF_rank.
Selecting MCP1 and standardising: MCP1_rank.
* changing MCP1 to numeric.
* standardising MCP1 (mean: 0.229468, n = 556).
* renaming RANK to MCP1_rank.
Selecting MIP1a and standardising: MIP1a_rank.
* changing MIP1a to numeric.
* standardising MIP1a (mean: 0.211721, n = 513).
* renaming RANK to MIP1a_rank.
Selecting RANTES and standardising: RANTES_rank.
* changing RANTES to numeric.
* standardising RANTES (mean: 0.228642, n = 554).
* renaming RANK to RANTES_rank.
Selecting MIG and standardising: MIG_rank.
* changing MIG to numeric.
* standardising MIG (mean: 0.226991, n = 550).
* renaming RANK to MIG_rank.
Selecting IP10 and standardising: IP10_rank.
* changing IP10 to numeric.
* standardising IP10 (mean: 0.205943, n = 499).
* renaming RANK to IP10_rank.
Selecting Eotaxin1 and standardising: Eotaxin1_rank.
* changing Eotaxin1 to numeric.
* standardising Eotaxin1 (mean: 0.233182, n = 565).
* renaming RANK to Eotaxin1_rank.
Selecting TARC and standardising: TARC_rank.
* changing TARC to numeric.
* standardising TARC (mean: 0.200578, n = 486).
* renaming RANK to TARC_rank.
Selecting PARC and standardising: PARC_rank.
* changing PARC to numeric.
* standardising PARC (mean: 0.233182, n = 565).
* renaming RANK to PARC_rank.
Selecting MDC and standardising: MDC_rank.
* changing MDC to numeric.
* standardising MDC (mean: 0.209657, n = 508).
* renaming RANK to MDC_rank.
Selecting OPG and standardising: OPG_rank.
* changing OPG to numeric.
* standardising OPG (mean: 0.232769, n = 564).
* renaming RANK to OPG_rank.
Selecting sICAM1 and standardising: sICAM1_rank.
* changing sICAM1 to numeric.
* standardising sICAM1 (mean: 0.233182, n = 565).
* renaming RANK to sICAM1_rank.
Selecting VEGFA and standardising: VEGFA_rank.
* changing VEGFA to numeric.
* standardising VEGFA (mean: 0.201403, n = 488).
* renaming RANK to VEGFA_rank.
Selecting TGFB and standardising: TGFB_rank.
* changing TGFB to numeric.
* standardising TGFB (mean: 0.22988, n = 557).
* renaming RANK to TGFB_rank.
Selecting MMP2 and standardising: MMP2_rank.
* changing MMP2 to numeric.
* standardising MMP2 (mean: 0.231944, n = 562).
* renaming RANK to MMP2_rank.
Selecting MMP8 and standardising: MMP8_rank.
* changing MMP8 to numeric.
* standardising MMP8 (mean: 0.231944, n = 562).
* renaming RANK to MMP8_rank.
Selecting MMP9 and standardising: MMP9_rank.
* changing MMP9 to numeric.
* standardising MMP9 (mean: 0.231531, n = 561).
* renaming RANK to MMP9_rank.# rm(var.temp, var.temp.rank)
Visualize transformations
We will just visualize these transformations.
proteins_of_interest_rank_mcp1 <- c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank", proteins_of_interest_rank)
proteins_of_interest_mcp1 <- c("MCP1_pg_ug_2015", "MCP1_pg_ml_2015", proteins_of_interest)
for(PROTEIN in proteins_of_interest_mcp1){
cat(paste0("Plotting protein ", PROTEIN, ".\n"))
p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN, " plaque levels"),
xlab = "",
ggtheme = theme_minimal())
print(p1)
}Plotting protein MCP1_pg_ug_2015.Using `bins = 30` by default. Pick better value with the argument `bins`.Plotting protein MCP1_pg_ml_2015.
Plotting protein IL2.
Plotting protein IL4.
Plotting protein IL5.
Plotting protein IL6.
Plotting protein IL8.
Plotting protein IL9.
Plotting protein IL10.
Plotting protein IL12.
Plotting protein IL13.
Plotting protein IL21.
Plotting protein INFG.
Plotting protein TNFA.
Plotting protein MIF.
Plotting protein MCP1.
Plotting protein MIP1a.
Plotting protein RANTES.
Plotting protein MIG.
Plotting protein IP10.
Plotting protein Eotaxin1.
Plotting protein TARC.
Plotting protein PARC.
Plotting protein MDC.
Plotting protein OPG.
Plotting protein sICAM1.
Plotting protein VEGFA.
Plotting protein TGFB.
Plotting protein MMP2.
Plotting protein MMP8.
Plotting protein MMP9.
for(PROTEIN in proteins_of_interest_rank_mcp1){
cat(paste0("Plotting protein ", PROTEIN, ".\n"))
p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
# y = "..count..",
color = "white",
fill = "Gender",
palette = c("#1290D9", "#DB003F"),
add = "mean",
# rug = TRUE,
# add.params = list(color = "black", linetype = 2),
title = paste0(PROTEIN, " plaque levels"),
xlab = "inverse-normal transformation",
ggtheme = theme_minimal())
print(p1)
}Plotting protein MCP1_pg_ug_2015_rank.Using `bins = 30` by default. Pick better value with the argument `bins`.Plotting protein MCP1_pg_ml_2015_rank.
Plotting protein IL2_rank.
Plotting protein IL4_rank.
Plotting protein IL5_rank.
Plotting protein IL6_rank.
Plotting protein IL8_rank.
Plotting protein IL9_rank.
Plotting protein IL10_rank.
Plotting protein IL12_rank.
Plotting protein IL13_rank.
Plotting protein IL21_rank.
Plotting protein INFG_rank.
Plotting protein TNFA_rank.
Plotting protein MIF_rank.
Plotting protein MCP1_rank.
Plotting protein MIP1a_rank.
Plotting protein RANTES_rank.
Plotting protein MIG_rank.
Plotting protein IP10_rank.
Plotting protein Eotaxin1_rank.
Plotting protein TARC_rank.
Plotting protein PARC_rank.
Plotting protein MDC_rank.
Plotting protein OPG_rank.
Plotting protein sICAM1_rank.
Plotting protein VEGFA_rank.
Plotting protein TGFB_rank.
Plotting protein MMP2_rank.
Plotting protein MMP8_rank.
Plotting protein MMP9_rank.
NACorrelations
Here we calculate correlations between MCP1_pg_ug_2015 and 28 other cytokines (including MCP1 as measured in experiment 1. We use Spearman’s test, thus, correlations a given in rho. Please note the indications of measurement methods:
- L: LUMINEX
- E: ELISA
- a: activity assay
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools))
install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")Skipping install of 'ggcorrplot' from a github remote, the SHA1 (c46b4cce) has not changed since last install.
Use `force = TRUE` to force installationlibrary(ggcorrplot)
# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA,
select = c(proteins_of_interest_rank_mcp1)
)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)
corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK,
use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
method = "spearman"), 3)
# corr_biomarkers.rank
rename_proteins_of_interest_mcp1 <- c("MCP1 (L, exp2, pg/ug)", "MCP1 (L, exp2, pg/mL)",
"IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12",
"IL13 (L)", "IL21 (L)",
"INFG", "TNFA", "MIF (L)",
"MCP1 (L, exp1)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)",
"Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)",
"OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")Cannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with tiesCannot compute exact p-value with ties# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
ut <- upper.tri(cormat)
data.frame(
row = rownames(cormat)[row(cormat)[ut]],
column = rownames(cormat)[col(cormat)[ut]],
cor =(cormat)[ut],
p = pmat[ut]
)
}
corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"
DT::datatable(corr_biomarkers.rank.df)
fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank,
method = "square",
type = "lower",
title = "Cross biomarker correlations",
show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
show.diag = TRUE,
hc.order = FALSE,
lab = FALSE,
digits = 3,
tl.cex = 6,
# xlab = c("MCP1"),
# p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())Saving 7.29 x 4.51 in imageggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(p1)
While visually actractive we are not necessarily interested in the correlations between all the cytokines, rather of MCP1 with other cytokines only.
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2, pg/ug)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold[1] 2.763428p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
fill = "CytokineY", # change fill color by cyl
# color = "white", # Set bar border colors to white
palette = uithof_color, # jco journal color palett. see ?ggpar
xlab = "Cytokine",
ylab = expression("Spearman's"~italic(rho)),
sort.val = "desc", # Sort the value in dscending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 45, # Rotate vertically x axis texts
cex = 0.8
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 9),
axis.text.y = element_text(size = 9)) 
rm(p1)
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2, pg/mL)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold[1] 2.763428p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
fill = "CytokineY", # change fill color by cyl
# color = "white", # Set bar border colors to white
palette = uithof_color, # jco journal color palett. see ?ggpar
xlab = "Cytokine",
ylab = expression("Spearman's"~italic(rho)),
sort.val = "desc", # Sort the value in dscending order
sort.by.groups = FALSE, # Don't sort inside each group
x.text.angle = 45, # Rotate vertically x axis texts
cex = 0.8
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 9),
axis.text.y = element_text(size = 9)) 
rm(p1)
Another version - problably not good.
p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
color = "CytokineY", # Color by groups
palette = uithof_color, # Custom color palette
xlab = "Cytokine",
ylab = expression(log[10]~"("~italic(p)~")-value"),
ylim = c(0, 6),
sorting = "descending", # Sort value in descending order
add = "segments", # Add segments from y = 0 to dots
rotate = FALSE, # Rotate vertically
# group = "CytokineY", # Order by groups
dot.size = 8, # Large dot size
label = round(temp$SpearmanRho, digits = 3), # Add mpg values as dot labels
font.label = list(color = "white", size = 8,
vjust = 0.5)
)
ggpar(p1, legend = "bottom",
legend.title = "") +
theme(axis.text.x = element_text(size = 9),
axis.text.y = element_text(size = 9))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.dotchart.MCP1_vs_Cytokines.png"), plot = last_plot())Saving 7.29 x 4.51 in imageggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.dotchart.MCP1_vs_Cytokines.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(temp, p1)
MCP1 vs. cytokines plaque levels lm()
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines traits as a function of plasma/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
630.7655 -0.3149
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9653 -0.6628 -0.1375 0.5011 3.1626
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 630.959461 118.334277 5.332 1.79e-07 ***
currentDF[, TRAIT] 0.030864 0.056196 0.549 0.583
Age 0.001691 0.006301 0.268 0.789
Gendermale 0.098331 0.118579 0.829 0.408
ORdate_year -0.315105 0.059084 -5.333 1.78e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.005 on 336 degrees of freedom
Multiple R-squared: 0.0827, Adjusted R-squared: 0.07178
F-statistic: 7.573 on 4 and 336 DF, p-value: 7.5e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL2_rank
Effect size...............: 0.030864
Standard error............: 0.056196
Odds ratio (effect size)..: 1.031
Lower 95% CI..............: 0.924
Upper 95% CI..............: 1.151
T-value...................: 0.549219
P-value...................: 0.5832202
R^2.......................: 0.082698
Adjusted r^2..............: 0.071778
Sample size of AE DB......: 2423
Sample size of model......: 341
Missing data %............: 85.92654
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
639.9380 -0.3195
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0641 -0.6896 -0.1302 0.4983 3.1035
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 653.999286 127.821331 5.117 5.46e-07 ***
currentDF[, TRAIT] -0.015511 0.058251 -0.266 0.790
Age 0.003407 0.006474 0.526 0.599
Gendermale 0.085706 0.124236 0.690 0.491
ORdate_year -0.326651 0.063815 -5.119 5.40e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.994 on 311 degrees of freedom
Multiple R-squared: 0.07901, Adjusted R-squared: 0.06717
F-statistic: 6.67 on 4 and 311 DF, p-value: 3.671e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.015511
Standard error............: 0.058251
Odds ratio (effect size)..: 0.985
Lower 95% CI..............: 0.878
Upper 95% CI..............: 1.104
T-value...................: -0.266271
P-value...................: 0.7902067
R^2.......................: 0.079014
Adjusted r^2..............: 0.067168
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
660.98 -0.33
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0554 -0.6780 -0.1180 0.5123 3.1079
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 672.363617 118.866914 5.656 3.33e-08 ***
currentDF[, TRAIT] 0.003866 0.054281 0.071 0.943
Age 0.003561 0.006106 0.583 0.560
Gendermale 0.124969 0.115465 1.082 0.280
ORdate_year -0.335830 0.059353 -5.658 3.30e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9734 on 333 degrees of freedom
Multiple R-squared: 0.09161, Adjusted R-squared: 0.0807
F-statistic: 8.396 on 4 and 333 DF, p-value: 1.833e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL5_rank
Effect size...............: 0.003866
Standard error............: 0.054281
Odds ratio (effect size)..: 1.004
Lower 95% CI..............: 0.903
Upper 95% CI..............: 1.117
T-value...................: 0.071219
P-value...................: 0.9432663
R^2.......................: 0.091613
Adjusted r^2..............: 0.080701
Sample size of AE DB......: 2423
Sample size of model......: 338
Missing data %............: 86.05035
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
832.4097 -0.4156
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9014 -0.7013 -0.1488 0.4951 3.4197
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 855.657335 114.883637 7.448 7.58e-13 ***
currentDF[, TRAIT] 0.073848 0.054959 1.344 0.180
Age 0.002869 0.006364 0.451 0.652
Gendermale 0.168691 0.118582 1.423 0.156
ORdate_year -0.427333 0.057368 -7.449 7.53e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.019 on 347 degrees of freedom
Multiple R-squared: 0.1421, Adjusted R-squared: 0.1322
F-statistic: 14.36 on 4 and 347 DF, p-value: 7.314e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.073848
Standard error............: 0.054959
Odds ratio (effect size)..: 1.077
Lower 95% CI..............: 0.967
Upper 95% CI..............: 1.199
T-value...................: 1.343696
P-value...................: 0.1799247
R^2.......................: 0.142056
Adjusted r^2..............: 0.132166
Sample size of AE DB......: 2423
Sample size of model......: 352
Missing data %............: 85.47255
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
949.0167 0.2155 -0.4738
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2020 -0.6699 -0.1392 0.5774 3.2808
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 944.761974 110.090988 8.582 3.48e-16 ***
currentDF[, TRAIT] 0.208541 0.055451 3.761 0.0002 ***
Age -0.003572 0.006226 -0.574 0.5665
Gendermale 0.161230 0.117705 1.370 0.1717
ORdate_year -0.471587 0.054970 -8.579 3.55e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9893 on 337 degrees of freedom
Multiple R-squared: 0.1945, Adjusted R-squared: 0.185
F-statistic: 20.35 on 4 and 337 DF, p-value: 5.004e-15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.208541
Standard error............: 0.055451
Odds ratio (effect size)..: 1.232
Lower 95% CI..............: 1.105
Upper 95% CI..............: 1.373
T-value...................: 3.760836
P-value...................: 0.0001996198
R^2.......................: 0.194514
Adjusted r^2..............: 0.184954
Sample size of AE DB......: 2423
Sample size of model......: 342
Missing data %............: 85.88527
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
622.4718 0.1003 -0.3108
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1249 -0.7621 -0.1436 0.5782 3.3210
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 618.950798 99.655729 6.211 1.40e-09 ***
currentDF[, TRAIT] 0.095343 0.056259 1.695 0.091 .
Age -0.004259 0.006375 -0.668 0.504
Gendermale 0.143711 0.118221 1.216 0.225
ORdate_year -0.308926 0.049752 -6.209 1.41e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.066 on 376 degrees of freedom
Multiple R-squared: 0.109, Adjusted R-squared: 0.09948
F-statistic: 11.49 on 4 and 376 DF, p-value: 8.188e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.095343
Standard error............: 0.056259
Odds ratio (effect size)..: 1.1
Lower 95% CI..............: 0.985
Upper 95% CI..............: 1.228
T-value...................: 1.694721
P-value...................: 0.09095642
R^2.......................: 0.108956
Adjusted r^2..............: 0.099476
Sample size of AE DB......: 2423
Sample size of model......: 381
Missing data %............: 84.27569
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
628.6785 -0.3139
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9975 -0.6684 -0.1695 0.5181 3.1334
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 624.142613 137.686327 4.533 8.45e-06 ***
currentDF[, TRAIT] 0.035466 0.061894 0.573 0.567
Age 0.001748 0.006658 0.263 0.793
Gendermale 0.153541 0.127939 1.200 0.231
ORdate_year -0.311724 0.068736 -4.535 8.37e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.008 on 296 degrees of freedom
Multiple R-squared: 0.07313, Adjusted R-squared: 0.06061
F-statistic: 5.839 on 4 and 296 DF, p-value: 0.0001554
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL10_rank
Effect size...............: 0.035466
Standard error............: 0.061894
Odds ratio (effect size)..: 1.036
Lower 95% CI..............: 0.918
Upper 95% CI..............: 1.17
T-value...................: 0.573016
P-value...................: 0.5670686
R^2.......................: 0.073133
Adjusted r^2..............: 0.060608
Sample size of AE DB......: 2423
Sample size of model......: 301
Missing data %............: 87.57738
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
673.4536 -0.3362
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9779 -0.6496 -0.1571 0.5102 3.1482
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 682.764977 128.777123 5.302 2.18e-07 ***
currentDF[, TRAIT] 0.026959 0.058149 0.464 0.643
Age 0.004435 0.006476 0.685 0.494
Gendermale 0.146685 0.122133 1.201 0.231
ORdate_year -0.341081 0.064294 -5.305 2.14e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9901 on 311 degrees of freedom
Multiple R-squared: 0.0897, Adjusted R-squared: 0.078
F-statistic: 7.662 on 4 and 311 DF, p-value: 6.721e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL12_rank
Effect size...............: 0.026959
Standard error............: 0.058149
Odds ratio (effect size)..: 1.027
Lower 95% CI..............: 0.917
Upper 95% CI..............: 1.151
T-value...................: 0.463623
P-value...................: 0.643242
R^2.......................: 0.089705
Adjusted r^2..............: 0.077997
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
641.7830 0.1242 -0.3204
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1224 -0.7231 -0.1614 0.5304 3.3659
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 639.064762 93.544453 6.832 2.89e-11 ***
currentDF[, TRAIT] 0.118799 0.051153 2.322 0.0207 *
Age -0.002429 0.005752 -0.422 0.6730
Gendermale 0.111908 0.106936 1.046 0.2959
ORdate_year -0.319018 0.046705 -6.830 2.91e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.029 on 429 degrees of freedom
Multiple R-squared: 0.1108, Adjusted R-squared: 0.1025
F-statistic: 13.36 on 4 and 429 DF, p-value: 2.878e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.118799
Standard error............: 0.051153
Odds ratio (effect size)..: 1.126
Lower 95% CI..............: 1.019
Upper 95% CI..............: 1.245
T-value...................: 2.322417
P-value...................: 0.02067766
R^2.......................: 0.110751
Adjusted r^2..............: 0.10246
Sample size of AE DB......: 2423
Sample size of model......: 434
Missing data %............: 82.08832
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
632.8733 0.1083 -0.3160
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1020 -0.7221 -0.1408 0.5203 3.3087
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 630.012001 93.343657 6.749 4.82e-11 ***
currentDF[, TRAIT] 0.101265 0.051574 1.963 0.0502 .
Age -0.002869 0.005727 -0.501 0.6167
Gendermale 0.109992 0.106895 1.029 0.3041
ORdate_year -0.314484 0.046603 -6.748 4.86e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.03 on 430 degrees of freedom
Multiple R-squared: 0.1075, Adjusted R-squared: 0.09922
F-statistic: 12.95 on 4 and 430 DF, p-value: 5.76e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.101265
Standard error............: 0.051574
Odds ratio (effect size)..: 1.107
Lower 95% CI..............: 1
Upper 95% CI..............: 1.224
T-value...................: 1.963494
P-value...................: 0.05023277
R^2.......................: 0.107526
Adjusted r^2..............: 0.099224
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
794.8567 0.1780 -0.3969
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9747 -0.6589 -0.1232 0.4988 3.1220
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.727e+02 1.236e+02 6.250 1.26e-09 ***
currentDF[, TRAIT] 3.325e-02 6.215e-02 0.535 0.593
Age 8.204e-05 6.345e-03 0.013 0.990
Gendermale 1.864e-01 1.207e-01 1.544 0.124
ORdate_year -3.858e-01 6.172e-02 -6.251 1.26e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.005 on 331 degrees of freedom
Multiple R-squared: 0.1277, Adjusted R-squared: 0.1171
F-statistic: 12.11 on 4 and 331 DF, p-value: 3.371e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: INFG_rank
Effect size...............: 0.033255
Standard error............: 0.062145
Odds ratio (effect size)..: 1.034
Lower 95% CI..............: 0.915
Upper 95% CI..............: 1.168
T-value...................: 0.535116
P-value...................: 0.5929293
R^2.......................: 0.127668
Adjusted r^2..............: 0.117126
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
777.2631 -0.3881
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9387 -0.6420 -0.1116 0.5372 3.1125
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 775.262571 126.839035 6.112 3.04e-09 ***
currentDF[, TRAIT] 0.038041 0.057218 0.665 0.507
Age 0.002251 0.006396 0.352 0.725
Gendermale 0.126709 0.120142 1.055 0.292
ORdate_year -0.387170 0.063334 -6.113 3.02e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9663 on 301 degrees of freedom
Multiple R-squared: 0.1175, Adjusted R-squared: 0.1057
F-statistic: 10.01 on 4 and 301 DF, p-value: 1.273e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TNFA_rank
Effect size...............: 0.038041
Standard error............: 0.057218
Odds ratio (effect size)..: 1.039
Lower 95% CI..............: 0.929
Upper 95% CI..............: 1.162
T-value...................: 0.66484
P-value...................: 0.5066621
R^2.......................: 0.117453
Adjusted r^2..............: 0.105725
Sample size of AE DB......: 2423
Sample size of model......: 306
Missing data %............: 87.37103
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
628.851 -0.314
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0279 -0.7193 -0.1231 0.5656 3.1094
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 587.953240 102.267860 5.749 1.70e-08 ***
currentDF[, TRAIT] 0.048322 0.054891 0.880 0.379
Age -0.003868 0.005720 -0.676 0.499
Gendermale 0.123082 0.107015 1.150 0.251
ORdate_year -0.293467 0.051048 -5.749 1.71e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.033 on 430 degrees of freedom
Multiple R-squared: 0.1011, Adjusted R-squared: 0.09278
F-statistic: 12.1 on 4 and 430 DF, p-value: 2.513e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.048322
Standard error............: 0.054891
Odds ratio (effect size)..: 1.05
Lower 95% CI..............: 0.942
Upper 95% CI..............: 1.169
T-value...................: 0.880332
P-value...................: 0.3791712
R^2.......................: 0.101144
Adjusted r^2..............: 0.092783
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
537.4404 0.2174 -0.2683
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2747 -0.7026 -0.1083 0.6341 2.9812
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 535.983520 94.005569 5.702 2.22e-08 ***
currentDF[, TRAIT] 0.211573 0.049722 4.255 2.57e-05 ***
Age -0.002903 0.005668 -0.512 0.609
Gendermale 0.093487 0.105953 0.882 0.378
ORdate_year -0.267556 0.046932 -5.701 2.23e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.013 on 426 degrees of freedom
Multiple R-squared: 0.1352, Adjusted R-squared: 0.1271
F-statistic: 16.65 on 4 and 426 DF, p-value: 1.081e-12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 0.211573
Standard error............: 0.049722
Odds ratio (effect size)..: 1.236
Lower 95% CI..............: 1.121
Upper 95% CI..............: 1.362
T-value...................: 4.255132
P-value...................: 2.571069e-05
R^2.......................: 0.135234
Adjusted r^2..............: 0.127114
Sample size of AE DB......: 2423
Sample size of model......: 431
Missing data %............: 82.21213
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
623.5912 0.1255 -0.3113
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1261 -0.7329 -0.1623 0.5681 3.2913
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 616.442600 97.731775 6.307 7.72e-10 ***
currentDF[, TRAIT] 0.117124 0.054539 2.148 0.0324 *
Age -0.005208 0.006201 -0.840 0.4015
Gendermale 0.124610 0.115692 1.077 0.2821
ORdate_year -0.307642 0.048795 -6.305 7.84e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.056 on 389 degrees of freedom
Multiple R-squared: 0.1107, Adjusted R-squared: 0.1016
F-statistic: 12.11 on 4 and 389 DF, p-value: 2.76e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.117124
Standard error............: 0.054539
Odds ratio (effect size)..: 1.124
Lower 95% CI..............: 1.01
Upper 95% CI..............: 1.251
T-value...................: 2.147531
P-value...................: 0.0323683
R^2.......................: 0.11072
Adjusted r^2..............: 0.101575
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
622.934 -0.311
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0175 -0.7403 -0.1353 0.5424 3.1722
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 585.819538 101.347154 5.780 1.45e-08 ***
currentDF[, TRAIT] 0.047304 0.054322 0.871 0.384
Age -0.003576 0.005797 -0.617 0.538
Gendermale 0.121548 0.108906 1.116 0.265
ORdate_year -0.292411 0.050589 -5.780 1.45e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.038 on 422 degrees of freedom
Multiple R-squared: 0.09946, Adjusted R-squared: 0.09092
F-statistic: 11.65 on 4 and 422 DF, p-value: 5.524e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.047304
Standard error............: 0.054322
Odds ratio (effect size)..: 1.048
Lower 95% CI..............: 0.943
Upper 95% CI..............: 1.166
T-value...................: 0.870798
P-value...................: 0.3843593
R^2.......................: 0.09946
Adjusted r^2..............: 0.090925
Sample size of AE DB......: 2423
Sample size of model......: 427
Missing data %............: 82.37722
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
664.19715 0.08683 -0.33160
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1225 -0.7536 -0.1288 0.5755 3.2629
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 655.579591 98.342266 6.666 8.29e-11 ***
currentDF[, TRAIT] 0.078663 0.053443 1.472 0.142
Age -0.004006 0.005904 -0.679 0.498
Gendermale 0.107600 0.109013 0.987 0.324
ORdate_year -0.327201 0.049105 -6.663 8.45e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.039 on 419 degrees of freedom
Multiple R-squared: 0.1029, Adjusted R-squared: 0.0943
F-statistic: 12.01 on 4 and 419 DF, p-value: 2.998e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.078663
Standard error............: 0.053443
Odds ratio (effect size)..: 1.082
Lower 95% CI..............: 0.974
Upper 95% CI..............: 1.201
T-value...................: 1.471907
P-value...................: 0.1417966
R^2.......................: 0.102865
Adjusted r^2..............: 0.094301
Sample size of AE DB......: 2423
Sample size of model......: 424
Missing data %............: 82.50103
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
627.7756 0.1526 -0.3134
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0450 -0.7399 -0.1309 0.6182 2.9704
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 625.783846 96.965266 6.454 3.30e-10 ***
currentDF[, TRAIT] 0.152242 0.052890 2.878 0.00422 **
Age -0.001851 0.006134 -0.302 0.76305
Gendermale 0.119062 0.112457 1.059 0.29039
ORdate_year -0.312420 0.048410 -6.454 3.31e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.031 on 383 degrees of freedom
Multiple R-squared: 0.1238, Adjusted R-squared: 0.1146
F-statistic: 13.53 on 4 and 383 DF, p-value: 2.526e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.152242
Standard error............: 0.05289
Odds ratio (effect size)..: 1.164
Lower 95% CI..............: 1.05
Upper 95% CI..............: 1.292
T-value...................: 2.878459
P-value...................: 0.004220329
R^2.......................: 0.123788
Adjusted r^2..............: 0.114637
Sample size of AE DB......: 2423
Sample size of model......: 388
Missing data %............: 83.98679
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
628.851 -0.314
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1188 -0.7504 -0.1295 0.5514 3.2214
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 635.173484 94.047905 6.754 4.69e-11 ***
currentDF[, TRAIT] 0.060590 0.052271 1.159 0.247
Age -0.003640 0.005724 -0.636 0.525
Gendermale 0.112562 0.107536 1.047 0.296
ORdate_year -0.317035 0.046956 -6.752 4.75e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.033 on 430 degrees of freedom
Multiple R-squared: 0.1023, Adjusted R-squared: 0.09398
F-statistic: 12.25 on 4 and 430 DF, p-value: 1.914e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.06059
Standard error............: 0.052271
Odds ratio (effect size)..: 1.062
Lower 95% CI..............: 0.959
Upper 95% CI..............: 1.177
T-value...................: 1.159153
P-value...................: 0.2470371
R^2.......................: 0.102329
Adjusted r^2..............: 0.093979
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
535.7557 0.1423 -0.2675
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0961 -0.7224 -0.1691 0.5283 3.3279
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 528.977983 114.965681 4.601 5.79e-06 ***
currentDF[, TRAIT] 0.137027 0.057409 2.387 0.0175 *
Age -0.005319 0.006054 -0.879 0.3802
Gendermale 0.097879 0.114654 0.854 0.3938
ORdate_year -0.263986 0.057388 -4.600 5.82e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.029 on 368 degrees of freedom
Multiple R-squared: 0.09194, Adjusted R-squared: 0.08207
F-statistic: 9.314 on 4 and 368 DF, p-value: 3.522e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.137027
Standard error............: 0.057409
Odds ratio (effect size)..: 1.147
Lower 95% CI..............: 1.025
Upper 95% CI..............: 1.283
T-value...................: 2.38685
P-value...................: 0.01749786
R^2.......................: 0.091935
Adjusted r^2..............: 0.082065
Sample size of AE DB......: 2423
Sample size of model......: 373
Missing data %............: 84.60586
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
564.3797 0.1192 -0.2818
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0187 -0.7131 -0.1376 0.5744 3.0323
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 560.191676 97.355447 5.754 1.66e-08 ***
currentDF[, TRAIT] 0.119650 0.052873 2.263 0.0241 *
Age -0.003899 0.005677 -0.687 0.4926
Gendermale 0.131785 0.106446 1.238 0.2164
ORdate_year -0.279617 0.048602 -5.753 1.66e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.028 on 430 degrees of freedom
Multiple R-squared: 0.1101, Adjusted R-squared: 0.1018
F-statistic: 13.3 on 4 and 430 DF, p-value: 3.15e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.11965
Standard error............: 0.052873
Odds ratio (effect size)..: 1.127
Lower 95% CI..............: 1.016
Upper 95% CI..............: 1.25
T-value...................: 2.262997
P-value...................: 0.02413382
R^2.......................: 0.110122
Adjusted r^2..............: 0.101844
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
638.0325 -0.3185
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0834 -0.7664 -0.1505 0.5932 3.1850
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 598.207223 104.616821 5.718 2.16e-08 ***
currentDF[, TRAIT] 0.049298 0.057782 0.853 0.394
Age -0.006564 0.006186 -1.061 0.289
Gendermale 0.142062 0.115942 1.225 0.221
ORdate_year -0.298498 0.052223 -5.716 2.19e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.061 on 386 degrees of freedom
Multiple R-squared: 0.1064, Adjusted R-squared: 0.09718
F-statistic: 11.5 on 4 and 386 DF, p-value: 7.941e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.049298
Standard error............: 0.057782
Odds ratio (effect size)..: 1.051
Lower 95% CI..............: 0.938
Upper 95% CI..............: 1.177
T-value...................: 0.853173
P-value...................: 0.3940926
R^2.......................: 0.106443
Adjusted r^2..............: 0.097183
Sample size of AE DB......: 2423
Sample size of model......: 391
Missing data %............: 83.86298
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
636.3065 0.1776 -0.3177
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8793 -0.7371 -0.1404 0.5705 3.2967
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 633.155178 92.631288 6.835 2.82e-11 ***
currentDF[, TRAIT] 0.172493 0.049001 3.520 0.000477 ***
Age -0.002665 0.005674 -0.470 0.638852
Gendermale 0.094668 0.106287 0.891 0.373600
ORdate_year -0.316055 0.046248 -6.834 2.85e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.021 on 429 degrees of freedom
Multiple R-squared: 0.1249, Adjusted R-squared: 0.1167
F-statistic: 15.3 on 4 and 429 DF, p-value: 1.048e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.172493
Standard error............: 0.049001
Odds ratio (effect size)..: 1.188
Lower 95% CI..............: 1.079
Upper 95% CI..............: 1.308
T-value...................: 3.520189
P-value...................: 0.0004773981
R^2.......................: 0.12485
Adjusted r^2..............: 0.11669
Sample size of AE DB......: 2423
Sample size of model......: 434
Missing data %............: 82.08832
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
589.99923 0.08682 -0.29458
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0531 -0.7372 -0.1295 0.5837 3.1438
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 588.749722 95.792010 6.146 1.81e-09 ***
currentDF[, TRAIT] 0.084207 0.050457 1.669 0.0959 .
Age -0.002997 0.005740 -0.522 0.6018
Gendermale 0.128363 0.106712 1.203 0.2297
ORdate_year -0.293896 0.047818 -6.146 1.81e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.031 on 430 degrees of freedom
Multiple R-squared: 0.1053, Adjusted R-squared: 0.097
F-statistic: 12.65 on 4 and 430 DF, p-value: 9.602e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.084207
Standard error............: 0.050457
Odds ratio (effect size)..: 1.088
Lower 95% CI..............: 0.985
Upper 95% CI..............: 1.201
T-value...................: 1.668876
P-value...................: 0.09586989
R^2.......................: 0.105319
Adjusted r^2..............: 0.096996
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
813.9742 0.1392 0.1678 -0.4064
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9349 -0.7199 -0.1693 0.4938 3.3124
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 809.904614 108.258059 7.481 5.60e-13 ***
currentDF[, TRAIT] 0.139589 0.054500 2.561 0.0108 *
Age -0.001632 0.005929 -0.275 0.7833
Gendermale 0.167548 0.113249 1.479 0.1399
ORdate_year -0.404337 0.054051 -7.481 5.62e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9887 on 363 degrees of freedom
Multiple R-squared: 0.1415, Adjusted R-squared: 0.132
F-statistic: 14.96 on 4 and 363 DF, p-value: 2.519e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.139589
Standard error............: 0.0545
Odds ratio (effect size)..: 1.15
Lower 95% CI..............: 1.033
Upper 95% CI..............: 1.279
T-value...................: 2.561258
P-value...................: 0.01083282
R^2.......................: 0.141488
Adjusted r^2..............: 0.132027
Sample size of AE DB......: 2423
Sample size of model......: 368
Missing data %............: 84.81222
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year, data = currentDF)
Coefficients:
(Intercept) ORdate_year
635.1303 -0.3171
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0022 -0.7461 -0.1141 0.5582 3.1030
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 604.033399 97.638090 6.186 1.46e-09 ***
currentDF[, TRAIT] 0.065295 0.052041 1.255 0.210
Age -0.003709 0.005813 -0.638 0.524
Gendermale 0.135163 0.110129 1.227 0.220
ORdate_year -0.301494 0.048747 -6.185 1.48e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.045 on 420 degrees of freedom
Multiple R-squared: 0.1033, Adjusted R-squared: 0.09472
F-statistic: 12.09 on 4 and 420 DF, p-value: 2.605e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.065295
Standard error............: 0.052041
Odds ratio (effect size)..: 1.067
Lower 95% CI..............: 0.964
Upper 95% CI..............: 1.182
T-value...................: 1.254703
P-value...................: 0.2102845
R^2.......................: 0.10326
Adjusted r^2..............: 0.094719
Sample size of AE DB......: 2423
Sample size of model......: 425
Missing data %............: 82.45976
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
753.7948 0.1213 0.2037 -0.3764
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1517 -0.6964 -0.1709 0.5491 3.2672
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 749.228529 98.935070 7.573 2.3e-13 ***
currentDF[, TRAIT] 0.118695 0.051879 2.288 0.0226 *
Age -0.003268 0.005687 -0.575 0.5659
Gendermale 0.203428 0.107494 1.892 0.0591 .
ORdate_year -0.374018 0.049390 -7.573 2.3e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.025 on 425 degrees of freedom
Multiple R-squared: 0.144, Adjusted R-squared: 0.1359
F-statistic: 17.87 on 4 and 425 DF, p-value: 1.431e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.118695
Standard error............: 0.051879
Odds ratio (effect size)..: 1.126
Lower 95% CI..............: 1.017
Upper 95% CI..............: 1.247
T-value...................: 2.28791
P-value...................: 0.02263308
R^2.......................: 0.14395
Adjusted r^2..............: 0.135894
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
777.5342 0.1525 -0.3882
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1931 -0.7112 -0.1256 0.5908 3.3015
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 773.198526 98.090158 7.883 2.72e-14 ***
currentDF[, TRAIT] 0.142473 0.051343 2.775 0.00576 **
Age -0.004326 0.005649 -0.766 0.44426
Gendermale 0.125413 0.107976 1.161 0.24609
ORdate_year -0.385917 0.048971 -7.881 2.76e-14 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.022 on 425 degrees of freedom
Multiple R-squared: 0.1488, Adjusted R-squared: 0.1408
F-statistic: 18.58 on 4 and 425 DF, p-value: 4.387e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.142473
Standard error............: 0.051343
Odds ratio (effect size)..: 1.153
Lower 95% CI..............: 1.043
Upper 95% CI..............: 1.275
T-value...................: 2.77494
P-value...................: 0.005764755
R^2.......................: 0.148829
Adjusted r^2..............: 0.140818
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
758.2047 0.0915 0.1571 -0.3786
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1310 -0.7113 -0.1531 0.5612 3.2432
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 750.404636 99.357633 7.553 2.64e-13 ***
currentDF[, TRAIT] 0.092850 0.050201 1.850 0.0651 .
Age -0.004751 0.005680 -0.836 0.4034
Gendermale 0.157370 0.107439 1.465 0.1437
ORdate_year -0.374540 0.049604 -7.551 2.67e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.027 on 425 degrees of freedom
Multiple R-squared: 0.1403, Adjusted R-squared: 0.1322
F-statistic: 17.34 on 4 and 425 DF, p-value: 3.425e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.09285
Standard error............: 0.050201
Odds ratio (effect size)..: 1.097
Lower 95% CI..............: 0.994
Upper 95% CI..............: 1.211
T-value...................: 1.849574
P-value...................: 0.06506902
R^2.......................: 0.140327
Adjusted r^2..............: 0.132236
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
Analysis of MCP1_pg_ml_2015_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
439.52974 -0.08325 0.21160 -0.21965
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0911 -0.5531 -0.0990 0.4573 2.6749
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 450.388268 105.057037 4.287 2.37e-05 ***
currentDF[, TRAIT] -0.079670 0.049891 -1.597 0.1112
Age 0.006158 0.005594 1.101 0.2718
Gendermale 0.207882 0.105274 1.975 0.0491 *
ORdate_year -0.225272 0.052455 -4.295 2.29e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8921 on 336 degrees of freedom
Multiple R-squared: 0.06631, Adjusted R-squared: 0.0552
F-statistic: 5.966 on 4 and 336 DF, p-value: 0.0001197
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.07967
Standard error............: 0.049891
Odds ratio (effect size)..: 0.923
Lower 95% CI..............: 0.837
Upper 95% CI..............: 1.018
T-value...................: -1.596881
P-value...................: 0.1112322
R^2.......................: 0.066314
Adjusted r^2..............: 0.055199
Sample size of AE DB......: 2423
Sample size of model......: 341
Missing data %............: 85.92654
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
540.5738 -0.1171 0.1848 -0.2701
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0091 -0.5472 -0.1075 0.4856 2.6161
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 549.80994 111.55762 4.928 1.35e-06 ***
currentDF[, TRAIT] -0.11149 0.05084 -2.193 0.0291 *
Age 0.00713 0.00565 1.262 0.2079
Gendermale 0.17874 0.10843 1.648 0.1003
ORdate_year -0.27492 0.05570 -4.936 1.30e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8675 on 311 degrees of freedom
Multiple R-squared: 0.09242, Adjusted R-squared: 0.08074
F-statistic: 7.917 on 4 and 311 DF, p-value: 4.345e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.111487
Standard error............: 0.050839
Odds ratio (effect size)..: 0.895
Lower 95% CI..............: 0.81
Upper 95% CI..............: 0.988
T-value...................: -2.192931
P-value...................: 0.02905265
R^2.......................: 0.092417
Adjusted r^2..............: 0.080744
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
503.0037 0.2535 -0.2513
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.99155 -0.54478 -0.07638 0.50816 2.54186
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 537.457180 106.206389 5.060 6.93e-07 ***
currentDF[, TRAIT] -0.058497 0.048500 -1.206 0.2286
Age 0.007261 0.005456 1.331 0.1842
Gendermale 0.238482 0.103166 2.312 0.0214 *
ORdate_year -0.268776 0.053031 -5.068 6.67e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8697 on 333 degrees of freedom
Multiple R-squared: 0.08729, Adjusted R-squared: 0.07632
F-statistic: 7.962 on 4 and 333 DF, p-value: 3.862e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.058497
Standard error............: 0.0485
Odds ratio (effect size)..: 0.943
Lower 95% CI..............: 0.858
Upper 95% CI..............: 1.037
T-value...................: -1.206137
P-value...................: 0.2286207
R^2.......................: 0.087288
Adjusted r^2..............: 0.076324
Sample size of AE DB......: 2423
Sample size of model......: 338
Missing data %............: 86.05035
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
599.930101 0.073020 0.008033 0.319352 -0.300019
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8218 -0.5577 -0.1269 0.4803 2.9552
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 599.930101 102.191239 5.871 1.02e-08 ***
currentDF[, TRAIT] 0.073020 0.048887 1.494 0.13617
Age 0.008033 0.005661 1.419 0.15682
Gendermale 0.319352 0.105481 3.028 0.00265 **
ORdate_year -0.300019 0.051030 -5.879 9.69e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9062 on 347 degrees of freedom
Multiple R-squared: 0.1111, Adjusted R-squared: 0.1008
F-statistic: 10.84 on 4 and 347 DF, p-value: 2.717e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.07302
Standard error............: 0.048887
Odds ratio (effect size)..: 1.076
Lower 95% CI..............: 0.977
Upper 95% CI..............: 1.184
T-value...................: 1.493662
P-value...................: 0.1361728
R^2.......................: 0.111087
Adjusted r^2..............: 0.10084
Sample size of AE DB......: 2423
Sample size of model......: 352
Missing data %............: 85.47255
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
697.4928 0.2632 0.3070 -0.3484
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1950 -0.5389 -0.0682 0.4695 2.8229
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 705.900501 95.458877 7.395 1.13e-12 ***
currentDF[, TRAIT] 0.260289 0.048081 5.414 1.18e-07 ***
Age 0.004083 0.005398 0.756 0.44998
Gendermale 0.309080 0.102061 3.028 0.00265 **
ORdate_year -0.352754 0.047664 -7.401 1.09e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8578 on 337 degrees of freedom
Multiple R-squared: 0.1946, Adjusted R-squared: 0.1851
F-statistic: 20.36 on 4 and 337 DF, p-value: 4.877e-15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.260289
Standard error............: 0.048081
Odds ratio (effect size)..: 1.297
Lower 95% CI..............: 1.181
Upper 95% CI..............: 1.426
T-value...................: 5.413579
P-value...................: 1.175805e-07
R^2.......................: 0.194641
Adjusted r^2..............: 0.185082
Sample size of AE DB......: 2423
Sample size of model......: 342
Missing data %............: 85.88527
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
392.8674 0.0749 0.3238 -0.1964
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0238 -0.5958 -0.1203 0.5313 2.9019
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 396.752588 88.227745 4.497 9.2e-06 ***
currentDF[, TRAIT] 0.077561 0.049808 1.557 0.1203
Age 0.002510 0.005644 0.445 0.6568
Gendermale 0.322682 0.104664 3.083 0.0022 **
ORdate_year -0.198406 0.044047 -4.504 8.9e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.944 on 376 degrees of freedom
Multiple R-squared: 0.07918, Adjusted R-squared: 0.06939
F-statistic: 8.083 on 4 and 376 DF, p-value: 2.921e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.077561
Standard error............: 0.049808
Odds ratio (effect size)..: 1.081
Lower 95% CI..............: 0.98
Upper 95% CI..............: 1.191
T-value...................: 1.557216
P-value...................: 0.1202604
R^2.......................: 0.079185
Adjusted r^2..............: 0.069389
Sample size of AE DB......: 2423
Sample size of model......: 381
Missing data %............: 84.27569
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
506.2501 0.2847 -0.2530
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.94534 -0.57186 -0.08303 0.49245 2.61786
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 532.461068 120.554405 4.417 1.41e-05 ***
currentDF[, TRAIT] -0.050056 0.054193 -0.924 0.3564
Age 0.006172 0.005829 1.059 0.2905
Gendermale 0.263904 0.112020 2.356 0.0191 *
ORdate_year -0.266274 0.060183 -4.424 1.36e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8827 on 296 degrees of freedom
Multiple R-squared: 0.08049, Adjusted R-squared: 0.06807
F-statistic: 6.478 on 4 and 296 DF, p-value: 5.213e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.050056
Standard error............: 0.054193
Odds ratio (effect size)..: 0.951
Lower 95% CI..............: 0.855
Upper 95% CI..............: 1.058
T-value...................: -0.923655
P-value...................: 0.356418
R^2.......................: 0.080495
Adjusted r^2..............: 0.068069
Sample size of AE DB......: 2423
Sample size of model......: 301
Missing data %............: 87.57738
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
548.41879 -0.08958 0.24946 -0.27403
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.96858 -0.54923 -0.09087 0.49552 2.65880
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 558.567899 112.807135 4.952 1.21e-06 ***
currentDF[, TRAIT] -0.083006 0.050937 -1.630 0.1042
Age 0.007858 0.005673 1.385 0.1670
Gendermale 0.236909 0.106987 2.214 0.0275 *
ORdate_year -0.279354 0.056321 -4.960 1.16e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8673 on 311 degrees of freedom
Multiple R-squared: 0.09053, Adjusted R-squared: 0.07883
F-statistic: 7.739 on 4 and 311 DF, p-value: 5.887e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.083006
Standard error............: 0.050937
Odds ratio (effect size)..: 0.92
Lower 95% CI..............: 0.833
Upper 95% CI..............: 1.017
T-value...................: -1.629574
P-value...................: 0.104204
R^2.......................: 0.090531
Adjusted r^2..............: 0.078833
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
408.84038 0.08096 0.26397 -0.20434
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.99901 -0.62198 -0.08861 0.53175 2.85876
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 417.755559 83.585739 4.998 8.46e-07 ***
currentDF[, TRAIT] 0.086418 0.045707 1.891 0.05934 .
Age 0.004672 0.005140 0.909 0.36389
Gendermale 0.260898 0.095552 2.730 0.00659 **
ORdate_year -0.208944 0.041733 -5.007 8.10e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9196 on 429 degrees of freedom
Multiple R-squared: 0.07553, Adjusted R-squared: 0.06691
F-statistic: 8.762 on 4 and 429 DF, p-value: 8.314e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.086418
Standard error............: 0.045707
Odds ratio (effect size)..: 1.09
Lower 95% CI..............: 0.997
Upper 95% CI..............: 1.192
T-value...................: 1.890677
P-value...................: 0.05934115
R^2.......................: 0.075528
Adjusted r^2..............: 0.066908
Sample size of AE DB......: 2423
Sample size of model......: 434
Missing data %............: 82.08832
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
404.39405 0.06671 0.26022 -0.20212
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.98297 -0.61654 -0.06537 0.53462 2.88093
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 412.360885 83.370793 4.946 1.09e-06 ***
currentDF[, TRAIT] 0.071706 0.046064 1.557 0.12028
Age 0.004476 0.005115 0.875 0.38204
Gendermale 0.257574 0.095475 2.698 0.00725 **
ORdate_year -0.206243 0.041624 -4.955 1.04e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9198 on 430 degrees of freedom
Multiple R-squared: 0.07309, Adjusted R-squared: 0.06447
F-statistic: 8.477 on 4 and 430 DF, p-value: 1.368e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.071706
Standard error............: 0.046064
Odds ratio (effect size)..: 1.074
Lower 95% CI..............: 0.982
Upper 95% CI..............: 1.176
T-value...................: 1.556674
P-value...................: 0.1202837
R^2.......................: 0.073091
Adjusted r^2..............: 0.064469
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
624.40653 -0.09079 0.29372 -0.31196
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.95225 -0.57294 -0.08409 0.49377 2.82516
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 633.635976 109.178351 5.804 1.52e-08 ***
currentDF[, TRAIT] -0.086460 0.054878 -1.576 0.11609
Age 0.007030 0.005603 1.255 0.21048
Gendermale 0.291357 0.106617 2.733 0.00662 **
ORdate_year -0.316806 0.054506 -5.812 1.45e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8871 on 331 degrees of freedom
Multiple R-squared: 0.1125, Adjusted R-squared: 0.1017
F-statistic: 10.49 on 4 and 331 DF, p-value: 5.21e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.08646
Standard error............: 0.054878
Odds ratio (effect size)..: 0.917
Lower 95% CI..............: 0.824
Upper 95% CI..............: 1.021
T-value...................: -1.575507
P-value...................: 0.1160946
R^2.......................: 0.11247
Adjusted r^2..............: 0.101745
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
550.1031 0.2622 -0.2749
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.89787 -0.54287 -0.04567 0.49646 2.58480
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 576.966996 112.824162 5.114 5.63e-07 ***
currentDF[, TRAIT] -0.046024 0.050896 -0.904 0.3666
Age 0.007178 0.005690 1.262 0.2081
Gendermale 0.244660 0.106867 2.289 0.0227 *
ORdate_year -0.288518 0.056336 -5.121 5.42e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8595 on 301 degrees of freedom
Multiple R-squared: 0.09809, Adjusted R-squared: 0.08611
F-statistic: 8.184 on 4 and 301 DF, p-value: 2.814e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TNFA_rank
Effect size...............: -0.046024
Standard error............: 0.050896
Odds ratio (effect size)..: 0.955
Lower 95% CI..............: 0.864
Upper 95% CI..............: 1.055
T-value...................: -0.904282
P-value...................: 0.3665688
R^2.......................: 0.098094
Adjusted r^2..............: 0.086109
Sample size of AE DB......: 2423
Sample size of model......: 306
Missing data %............: 87.37103
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
402.1448 0.2706 -0.2010
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.91129 -0.60262 -0.06228 0.55583 2.96602
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 357.525975 91.070212 3.926 0.000101 ***
currentDF[, TRAIT] 0.067646 0.048881 1.384 0.167107
Age 0.004041 0.005094 0.793 0.428030
Gendermale 0.264634 0.095298 2.777 0.005727 **
ORdate_year -0.178868 0.045459 -3.935 9.71e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9203 on 430 degrees of freedom
Multiple R-squared: 0.072, Adjusted R-squared: 0.06337
F-statistic: 8.341 on 4 and 430 DF, p-value: 1.737e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.067646
Standard error............: 0.048881
Odds ratio (effect size)..: 1.07
Lower 95% CI..............: 0.972
Upper 95% CI..............: 1.178
T-value...................: 1.383899
P-value...................: 0.1671073
R^2.......................: 0.072001
Adjusted r^2..............: 0.063368
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
305.3839 0.2222 0.2393 -0.1527
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1835 -0.6174 -0.0939 0.5408 2.9516
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 313.648565 82.705400 3.792 0.000171 ***
currentDF[, TRAIT] 0.225302 0.043745 5.150 3.98e-07 ***
Age 0.004970 0.004986 0.997 0.319495
Gendermale 0.236381 0.093216 2.536 0.011574 *
ORdate_year -0.156994 0.041291 -3.802 0.000164 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8911 on 426 degrees of freedom
Multiple R-squared: 0.1224, Adjusted R-squared: 0.1142
F-statistic: 14.86 on 4 and 426 DF, p-value: 2.248e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 0.225302
Standard error............: 0.043745
Odds ratio (effect size)..: 1.253
Lower 95% CI..............: 1.15
Upper 95% CI..............: 1.365
T-value...................: 5.150371
P-value...................: 3.98173e-07
R^2.......................: 0.122428
Adjusted r^2..............: 0.114188
Sample size of AE DB......: 2423
Sample size of model......: 431
Missing data %............: 82.21213
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
393.90745 0.08625 0.30123 -0.19690
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0188 -0.6276 -0.0954 0.5264 2.8731
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 396.969693 86.616258 4.583 6.18e-06 ***
currentDF[, TRAIT] 0.087714 0.048336 1.815 0.07034 .
Age 0.001742 0.005496 0.317 0.75143
Gendermale 0.300124 0.102533 2.927 0.00362 **
ORdate_year -0.198485 0.043245 -4.590 6.00e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9358 on 389 degrees of freedom
Multiple R-squared: 0.07865, Adjusted R-squared: 0.06917
F-statistic: 8.301 on 4 and 389 DF, p-value: 1.964e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.087714
Standard error............: 0.048336
Odds ratio (effect size)..: 1.092
Lower 95% CI..............: 0.993
Upper 95% CI..............: 1.2
T-value...................: 1.814678
P-value...................: 0.0703435
R^2.......................: 0.078646
Adjusted r^2..............: 0.069172
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
327.62609 0.09623 0.26629 -0.16381
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.87164 -0.58387 -0.08473 0.52960 2.97018
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 332.983028 89.291714 3.729 0.000218 ***
currentDF[, TRAIT] 0.101528 0.047860 2.121 0.034475 *
Age 0.004957 0.005107 0.971 0.332343
Gendermale 0.262940 0.095951 2.740 0.006398 **
ORdate_year -0.166649 0.044571 -3.739 0.000210 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9144 on 422 degrees of freedom
Multiple R-squared: 0.07764, Adjusted R-squared: 0.0689
F-statistic: 8.881 on 4 and 422 DF, p-value: 6.824e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.101528
Standard error............: 0.04786
Odds ratio (effect size)..: 1.107
Lower 95% CI..............: 1.008
Upper 95% CI..............: 1.216
T-value...................: 2.121344
P-value...................: 0.03447519
R^2.......................: 0.077641
Adjusted r^2..............: 0.068898
Sample size of AE DB......: 2423
Sample size of model......: 427
Missing data %............: 82.37722
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
404.8686 0.2736 -0.2024
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9925 -0.5977 -0.1095 0.5154 2.9297
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 427.718328 87.574695 4.884 1.48e-06 ***
currentDF[, TRAIT] 0.036122 0.047591 0.759 0.44828
Age 0.003277 0.005258 0.623 0.53344
Gendermale 0.267777 0.097077 2.758 0.00606 **
ORdate_year -0.213864 0.043728 -4.891 1.43e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.925 on 419 degrees of freedom
Multiple R-squared: 0.06996, Adjusted R-squared: 0.06108
F-statistic: 7.879 on 4 and 419 DF, p-value: 3.944e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.036122
Standard error............: 0.047591
Odds ratio (effect size)..: 1.037
Lower 95% CI..............: 0.944
Upper 95% CI..............: 1.138
T-value...................: 0.758996
P-value...................: 0.4482814
R^2.......................: 0.069959
Adjusted r^2..............: 0.06108
Sample size of AE DB......: 2423
Sample size of model......: 424
Missing data %............: 82.50103
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
400.3374 0.1025 0.2924 -0.2001
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.94733 -0.58274 -0.09808 0.53081 2.89664
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 407.032341 86.721660 4.694 3.74e-06 ***
currentDF[, TRAIT] 0.106331 0.047303 2.248 0.02515 *
Age 0.004060 0.005486 0.740 0.45972
Gendermale 0.289105 0.100577 2.874 0.00427 **
ORdate_year -0.203589 0.043296 -4.702 3.60e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9225 on 383 degrees of freedom
Multiple R-squared: 0.08553, Adjusted R-squared: 0.07598
F-statistic: 8.956 on 4 and 383 DF, p-value: 6.362e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.106331
Standard error............: 0.047303
Odds ratio (effect size)..: 1.112
Lower 95% CI..............: 1.014
Upper 95% CI..............: 1.22
T-value...................: 2.247891
P-value...................: 0.02515147
R^2.......................: 0.085535
Adjusted r^2..............: 0.075984
Sample size of AE DB......: 2423
Sample size of model......: 388
Missing data %............: 83.98679
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
402.1448 0.2706 -0.2010
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0014 -0.6096 -0.1007 0.5052 2.9228
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 417.170515 83.883143 4.973 9.53e-07 ***
currentDF[, TRAIT] 0.049261 0.046621 1.057 0.29128
Age 0.003995 0.005106 0.783 0.43434
Gendermale 0.257956 0.095913 2.689 0.00744 **
ORdate_year -0.208627 0.041881 -4.981 9.16e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9212 on 430 degrees of freedom
Multiple R-squared: 0.07028, Adjusted R-squared: 0.06163
F-statistic: 8.126 on 4 and 430 DF, p-value: 2.527e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.049261
Standard error............: 0.046621
Odds ratio (effect size)..: 1.05
Lower 95% CI..............: 0.959
Upper 95% CI..............: 1.151
T-value...................: 1.056626
P-value...................: 0.2912753
R^2.......................: 0.070282
Adjusted r^2..............: 0.061633
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
259.7313 0.1304 0.2372 -0.1299
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9737 -0.6141 -0.1072 0.5032 2.6997
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 266.159979 102.621677 2.594 0.00988 **
currentDF[, TRAIT] 0.133654 0.051245 2.608 0.00947 **
Age 0.003268 0.005404 0.605 0.54568
Gendermale 0.235606 0.102344 2.302 0.02189 *
ORdate_year -0.133251 0.051227 -2.601 0.00966 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9182 on 368 degrees of freedom
Multiple R-squared: 0.05708, Adjusted R-squared: 0.04683
F-statistic: 5.569 on 4 and 368 DF, p-value: 0.0002314
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.133654
Standard error............: 0.051245
Odds ratio (effect size)..: 1.143
Lower 95% CI..............: 1.034
Upper 95% CI..............: 1.264
T-value...................: 2.608132
P-value...................: 0.009474563
R^2.......................: 0.057077
Adjusted r^2..............: 0.046827
Sample size of AE DB......: 2423
Sample size of model......: 373
Missing data %............: 84.60586
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
327.4082 0.1384 0.2771 -0.1637
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9070 -0.6184 -0.0725 0.5392 3.0240
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 333.667657 86.443553 3.860 0.000131 ***
currentDF[, TRAIT] 0.139435 0.046946 2.970 0.003144 **
Age 0.003913 0.005040 0.776 0.437991
Gendermale 0.275525 0.094515 2.915 0.003741 **
ORdate_year -0.166963 0.043154 -3.869 0.000126 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.913 on 430 degrees of freedom
Multiple R-squared: 0.08661, Adjusted R-squared: 0.07811
F-statistic: 10.19 on 4 and 430 DF, p-value: 6.827e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.139435
Standard error............: 0.046946
Odds ratio (effect size)..: 1.15
Lower 95% CI..............: 1.049
Upper 95% CI..............: 1.26
T-value...................: 2.970091
P-value...................: 0.003143855
R^2.......................: 0.086606
Adjusted r^2..............: 0.078109
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
408.0877 0.3213 -0.2040
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.99814 -0.59702 -0.08428 0.55183 2.92191
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 374.573357 92.251811 4.060 5.94e-05 ***
currentDF[, TRAIT] 0.057757 0.050953 1.134 0.25769
Age 0.001495 0.005455 0.274 0.78413
Gendermale 0.323557 0.102239 3.165 0.00168 **
ORdate_year -0.187304 0.046050 -4.067 5.77e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9354 on 386 degrees of freedom
Multiple R-squared: 0.07845, Adjusted R-squared: 0.0689
F-statistic: 8.215 on 4 and 386 DF, p-value: 2.29e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.057757
Standard error............: 0.050953
Odds ratio (effect size)..: 1.059
Lower 95% CI..............: 0.959
Upper 95% CI..............: 1.171
T-value...................: 1.133543
P-value...................: 0.2576896
R^2.......................: 0.078453
Adjusted r^2..............: 0.068903
Sample size of AE DB......: 2423
Sample size of model......: 391
Missing data %............: 83.86298
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
406.1916 0.1514 0.2463 -0.2030
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7926 -0.6021 -0.1021 0.5371 2.8144
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 414.852239 82.585723 5.023 7.47e-07 ***
currentDF[, TRAIT] 0.154488 0.043687 3.536 0.00045 ***
Age 0.004756 0.005058 0.940 0.34758
Gendermale 0.243240 0.094761 2.567 0.01060 *
ORdate_year -0.207492 0.041233 -5.032 7.14e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9102 on 429 degrees of freedom
Multiple R-squared: 0.09423, Adjusted R-squared: 0.08578
F-statistic: 11.16 on 4 and 429 DF, p-value: 1.28e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.154488
Standard error............: 0.043687
Odds ratio (effect size)..: 1.167
Lower 95% CI..............: 1.071
Upper 95% CI..............: 1.271
T-value...................: 3.536236
P-value...................: 0.0004500912
R^2.......................: 0.094227
Adjusted r^2..............: 0.085781
Sample size of AE DB......: 2423
Sample size of model......: 434
Missing data %............: 82.08832
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
348.5754 0.1199 0.2743 -0.1743
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.94630 -0.60443 -0.07322 0.52075 2.92462
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 355.162519 84.910547 4.183 3.49e-05 ***
currentDF[, TRAIT] 0.126150 0.044725 2.821 0.00502 **
Age 0.005385 0.005088 1.058 0.29046
Gendermale 0.272232 0.094590 2.878 0.00420 **
ORdate_year -0.177736 0.042386 -4.193 3.34e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9139 on 430 degrees of freedom
Multiple R-squared: 0.0848, Adjusted R-squared: 0.07629
F-statistic: 9.961 on 4 and 430 DF, p-value: 1.023e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.12615
Standard error............: 0.044725
Odds ratio (effect size)..: 1.134
Lower 95% CI..............: 1.039
Upper 95% CI..............: 1.238
T-value...................: 2.820549
P-value...................: 0.00501602
R^2.......................: 0.0848
Adjusted r^2..............: 0.076287
Sample size of AE DB......: 2423
Sample size of model......: 435
Missing data %............: 82.04705
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
625.1068 0.2088 0.2828 -0.3123
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8013 -0.5778 -0.1391 0.4550 2.8829
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 637.536834 95.001315 6.711 7.46e-11 ***
currentDF[, TRAIT] 0.207733 0.047826 4.343 1.82e-05 ***
Age 0.004984 0.005203 0.958 0.33876
Gendermale 0.283464 0.099381 2.852 0.00459 **
ORdate_year -0.318649 0.047432 -6.718 7.14e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8676 on 363 degrees of freedom
Multiple R-squared: 0.1388, Adjusted R-squared: 0.1293
F-statistic: 14.62 on 4 and 363 DF, p-value: 4.399e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.207733
Standard error............: 0.047826
Odds ratio (effect size)..: 1.231
Lower 95% CI..............: 1.121
Upper 95% CI..............: 1.352
T-value...................: 4.343473
P-value...................: 1.822187e-05
R^2.......................: 0.138758
Adjusted r^2..............: 0.129267
Sample size of AE DB......: 2423
Sample size of model......: 368
Missing data %............: 84.81222
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
433.2702 0.2619 -0.2165
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9163 -0.6071 -0.1017 0.5428 2.9677
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 420.271319 86.614707 4.852 1.72e-06 ***
currentDF[, TRAIT] 0.048956 0.046165 1.060 0.28954
Age 0.003353 0.005156 0.650 0.51589
Gendermale 0.264868 0.097695 2.711 0.00698 **
ORdate_year -0.210145 0.043243 -4.860 1.66e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9269 on 420 degrees of freedom
Multiple R-squared: 0.07633, Adjusted R-squared: 0.06754
F-statistic: 8.677 on 4 and 420 DF, p-value: 9.758e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.048956
Standard error............: 0.046165
Odds ratio (effect size)..: 1.05
Lower 95% CI..............: 0.959
Upper 95% CI..............: 1.15
T-value...................: 1.060461
P-value...................: 0.2895444
R^2.......................: 0.076334
Adjusted r^2..............: 0.067537
Sample size of AE DB......: 2423
Sample size of model......: 425
Missing data %............: 82.45976
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
563.6482 0.3221 -0.2816
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9560 -0.5839 -0.0973 0.5118 3.1719
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 559.51950 88.02210 6.357 5.33e-10 ***
currentDF[, TRAIT] 0.03617 0.04616 0.784 0.433740
Age 0.00240 0.00506 0.474 0.635456
Gendermale 0.33046 0.09564 3.455 0.000605 ***
ORdate_year -0.27966 0.04394 -6.364 5.09e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9122 on 425 degrees of freedom
Multiple R-squared: 0.1113, Adjusted R-squared: 0.1029
F-statistic: 13.31 on 4 and 425 DF, p-value: 3.182e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.036166
Standard error............: 0.046157
Odds ratio (effect size)..: 1.037
Lower 95% CI..............: 0.947
Upper 95% CI..............: 1.135
T-value...................: 0.78355
P-value...................: 0.4337405
R^2.......................: 0.111296
Adjusted r^2..............: 0.102932
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
563.2526 0.1837 0.2579 -0.2814
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.14127 -0.54874 -0.08888 0.49583 3.08371
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 566.655197 85.910098 6.596 1.26e-10 ***
currentDF[, TRAIT] 0.183850 0.044968 4.089 5.19e-05 ***
Age 0.002163 0.004947 0.437 0.66212
Gendermale 0.257646 0.094569 2.724 0.00671 **
ORdate_year -0.283185 0.042890 -6.603 1.21e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8954 on 425 degrees of freedom
Multiple R-squared: 0.1437, Adjusted R-squared: 0.1356
F-statistic: 17.83 on 4 and 425 DF, p-value: 1.523e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.18385
Standard error............: 0.044968
Odds ratio (effect size)..: 1.202
Lower 95% CI..............: 1.1
Upper 95% CI..............: 1.313
T-value...................: 4.088516
P-value...................: 5.192815e-05
R^2.......................: 0.143692
Adjusted r^2..............: 0.135633
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
536.0842 0.1141 0.3002 -0.2679
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0536 -0.5754 -0.1183 0.5094 3.0413
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 538.771644 87.598569 6.150 1.78e-09 ***
currentDF[, TRAIT] 0.113628 0.044259 2.567 0.01059 *
Age 0.001637 0.005008 0.327 0.74392
Gendermale 0.300070 0.094724 3.168 0.00165 **
ORdate_year -0.269268 0.043733 -6.157 1.72e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9058 on 425 degrees of freedom
Multiple R-squared: 0.1236, Adjusted R-squared: 0.1154
F-statistic: 14.99 on 4 and 425 DF, p-value: 1.817e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.113628
Standard error............: 0.044259
Odds ratio (effect size)..: 1.12
Lower 95% CI..............: 1.027
Upper 95% CI..............: 1.222
T-value...................: 2.567334
P-value...................: 0.01058951
R^2.......................: 0.123604
Adjusted r^2..............: 0.115355
Sample size of AE DB......: 2423
Sample size of model......: 430
Missing data %............: 82.25341
Analysis of MCP1_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
317.7837 0.2414 -0.1587
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3756 -0.6561 -0.0206 0.6542 2.6675
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 331.200461 103.072538 3.213 0.00142 **
currentDF[, TRAIT] -0.067438 0.048698 -1.385 0.16687
Age -0.005142 0.005524 -0.931 0.35245
Gendermale 0.234123 0.107979 2.168 0.03073 *
ORdate_year -0.165194 0.051462 -3.210 0.00143 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9691 on 403 degrees of freedom
Multiple R-squared: 0.03965, Adjusted R-squared: 0.03011
F-statistic: 4.159 on 4 and 403 DF, p-value: 0.002591
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.067438
Standard error............: 0.048698
Odds ratio (effect size)..: 0.935
Lower 95% CI..............: 0.85
Upper 95% CI..............: 1.028
T-value...................: -1.384828
P-value...................: 0.1668713
R^2.......................: 0.039647
Adjusted r^2..............: 0.030115
Sample size of AE DB......: 2423
Sample size of model......: 408
Missing data %............: 83.16137
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
396.488 0.302 -0.198
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3451 -0.6586 -0.0216 0.6565 2.7193
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 395.419104 111.526359 3.546 0.000442 ***
currentDF[, TRAIT] -0.039830 0.050847 -0.783 0.433930
Age -0.006173 0.005785 -1.067 0.286646
Gendermale 0.299567 0.113355 2.643 0.008571 **
ORdate_year -0.197249 0.055683 -3.542 0.000447 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9702 on 373 degrees of freedom
Multiple R-squared: 0.05331, Adjusted R-squared: 0.04316
F-statistic: 5.251 on 4 and 373 DF, p-value: 0.0003999
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.03983
Standard error............: 0.050847
Odds ratio (effect size)..: 0.961
Lower 95% CI..............: 0.87
Upper 95% CI..............: 1.062
T-value...................: -0.783331
P-value...................: 0.43393
R^2.......................: 0.053309
Adjusted r^2..............: 0.043157
Sample size of AE DB......: 2423
Sample size of model......: 378
Missing data %............: 84.39951
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
384.48883 -0.07448 0.28809 -0.19198
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4804 -0.6335 -0.0382 0.6628 2.6508
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 369.073757 107.080598 3.447 0.000628 ***
currentDF[, TRAIT] -0.075767 0.048993 -1.546 0.122783
Age -0.007120 0.005603 -1.271 0.204576
Gendermale 0.299056 0.108116 2.766 0.005939 **
ORdate_year -0.184049 0.053468 -3.442 0.000638 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9656 on 396 degrees of freedom
Multiple R-squared: 0.05401, Adjusted R-squared: 0.04446
F-statistic: 5.653 on 4 and 396 DF, p-value: 0.0001964
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.075767
Standard error............: 0.048993
Odds ratio (effect size)..: 0.927
Lower 95% CI..............: 0.842
Upper 95% CI..............: 1.02
T-value...................: -1.546497
P-value...................: 0.1227834
R^2.......................: 0.054014
Adjusted r^2..............: 0.044458
Sample size of AE DB......: 2423
Sample size of model......: 401
Missing data %............: 83.45027
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
304.00325 0.06981 0.22677 -0.15178
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.15866 -0.65200 -0.00741 0.66318 2.69318
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 295.809907 97.929224 3.021 0.00268 **
currentDF[, TRAIT] 0.069665 0.047370 1.471 0.14213
Age -0.005063 0.005497 -0.921 0.35752
Gendermale 0.230037 0.106708 2.156 0.03167 *
ORdate_year -0.147522 0.048896 -3.017 0.00271 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9719 on 420 degrees of freedom
Multiple R-squared: 0.03519, Adjusted R-squared: 0.026
F-statistic: 3.83 on 4 and 420 DF, p-value: 0.004536
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.069665
Standard error............: 0.04737
Odds ratio (effect size)..: 1.072
Lower 95% CI..............: 0.977
Upper 95% CI..............: 1.176
T-value...................: 1.470669
P-value...................: 0.1421293
R^2.......................: 0.035189
Adjusted r^2..............: 0.026
Sample size of AE DB......: 2423
Sample size of model......: 425
Missing data %............: 82.45976
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
455.41647 0.31925 -0.01045 0.22219 -0.22697
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.15896 -0.54787 -0.05189 0.56747 2.83787
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 455.416470 91.719536 4.965 1.01e-06 ***
currentDF[, TRAIT] 0.319248 0.047186 6.766 4.63e-11 ***
Age -0.010455 0.005209 -2.007 0.0454 *
Gendermale 0.222191 0.102155 2.175 0.0302 *
ORdate_year -0.226974 0.045788 -4.957 1.05e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9134 on 407 degrees of freedom
Multiple R-squared: 0.1467, Adjusted R-squared: 0.1383
F-statistic: 17.49 on 4 and 407 DF, p-value: 2.941e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.319248
Standard error............: 0.047186
Odds ratio (effect size)..: 1.376
Lower 95% CI..............: 1.255
Upper 95% CI..............: 1.509
T-value...................: 6.765783
P-value...................: 4.631788e-11
R^2.......................: 0.146701
Adjusted r^2..............: 0.138315
Sample size of AE DB......: 2423
Sample size of model......: 412
Missing data %............: 82.99629
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
449.5529 0.2728 0.2753 -0.2244
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.01604 -0.62630 -0.08902 0.62907 2.66902
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 445.416806 71.222529 6.254 8.66e-10 ***
currentDF[, TRAIT] 0.269238 0.040045 6.723 4.91e-11 ***
Age -0.004330 0.004633 -0.935 0.35047
Gendermale 0.277452 0.088478 3.136 0.00182 **
ORdate_year -0.222210 0.035552 -6.250 8.85e-10 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8896 on 495 degrees of freedom
Multiple R-squared: 0.1682, Adjusted R-squared: 0.1614
F-statistic: 25.02 on 4 and 495 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.269238
Standard error............: 0.040045
Odds ratio (effect size)..: 1.309
Lower 95% CI..............: 1.21
Upper 95% CI..............: 1.416
T-value...................: 6.723338
P-value...................: 4.907886e-11
R^2.......................: 0.168152
Adjusted r^2..............: 0.16143
Sample size of AE DB......: 2423
Sample size of model......: 500
Missing data %............: 79.36442
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
406.1557 -0.1134 0.2844 -0.2028
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4859 -0.6531 -0.0240 0.7019 2.6509
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 398.869702 119.403549 3.341 0.000926 ***
currentDF[, TRAIT] -0.117134 0.053607 -2.185 0.029543 *
Age -0.007922 0.005967 -1.328 0.185151
Gendermale 0.298223 0.116386 2.562 0.010810 *
ORdate_year -0.198902 0.059610 -3.337 0.000938 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9788 on 353 degrees of freedom
Multiple R-squared: 0.05845, Adjusted R-squared: 0.04778
F-statistic: 5.478 on 4 and 353 DF, p-value: 0.0002735
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.117134
Standard error............: 0.053607
Odds ratio (effect size)..: 0.889
Lower 95% CI..............: 0.801
Upper 95% CI..............: 0.988
T-value...................: -2.185044
P-value...................: 0.02954261
R^2.......................: 0.058451
Adjusted r^2..............: 0.047781
Sample size of AE DB......: 2423
Sample size of model......: 358
Missing data %............: 85.22493
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
393.10801 -0.09786 0.30519 -0.19629
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5214 -0.6373 -0.0335 0.6609 2.6820
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 385.201779 111.303850 3.461 0.000601 ***
currentDF[, TRAIT] -0.099846 0.050664 -1.971 0.049487 *
Age -0.005609 0.005764 -0.973 0.331137
Gendermale 0.316927 0.111700 2.837 0.004797 **
ORdate_year -0.192164 0.055571 -3.458 0.000607 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.966 on 374 degrees of freedom
Multiple R-squared: 0.05558, Adjusted R-squared: 0.04548
F-statistic: 5.503 on 4 and 374 DF, p-value: 0.0002583
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.099846
Standard error............: 0.050664
Odds ratio (effect size)..: 0.905
Lower 95% CI..............: 0.819
Upper 95% CI..............: 0.999
T-value...................: -1.970771
P-value...................: 0.04948712
R^2.......................: 0.055585
Adjusted r^2..............: 0.045484
Sample size of AE DB......: 2423
Sample size of model......: 379
Missing data %............: 84.35823
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
464.1716 0.4014 0.2097 -0.2317
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6625 -0.6651 -0.0686 0.5793 2.4990
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 462.277252 69.049724 6.695 5.34e-11 ***
currentDF[, TRAIT] 0.400238 0.037890 10.563 < 2e-16 ***
Age -0.001378 0.004368 -0.315 0.7525
Gendermale 0.210670 0.083341 2.528 0.0118 *
ORdate_year -0.230718 0.034471 -6.693 5.40e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8808 on 550 degrees of freedom
Multiple R-squared: 0.2245, Adjusted R-squared: 0.2189
F-statistic: 39.81 on 4 and 550 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.400238
Standard error............: 0.03789
Odds ratio (effect size)..: 1.492
Lower 95% CI..............: 1.385
Upper 95% CI..............: 1.607
T-value...................: 10.56311
P-value...................: 7.144811e-24
R^2.......................: 0.224521
Adjusted r^2..............: 0.218881
Sample size of AE DB......: 2423
Sample size of model......: 555
Missing data %............: 77.09451
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
436.2035 0.3523 0.2078 -0.2178
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5943 -0.6710 -0.0755 0.6157 2.3777
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 432.376101 70.746565 6.112 1.87e-09 ***
currentDF[, TRAIT] 0.350079 0.038816 9.019 < 2e-16 ***
Age -0.002970 0.004468 -0.665 0.5065
Gendermale 0.209696 0.085522 2.452 0.0145 *
ORdate_year -0.215744 0.035317 -6.109 1.90e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9045 on 551 degrees of freedom
Multiple R-squared: 0.1873, Adjusted R-squared: 0.1814
F-statistic: 31.76 on 4 and 551 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.350079
Standard error............: 0.038816
Odds ratio (effect size)..: 1.419
Lower 95% CI..............: 1.315
Upper 95% CI..............: 1.531
T-value...................: 9.018962
P-value...................: 3.135903e-18
R^2.......................: 0.187347
Adjusted r^2..............: 0.181448
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
435.5907 0.3678 -0.2175
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4331 -0.6404 -0.0233 0.6720 2.7166
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 471.147317 106.339563 4.431 1.21e-05 ***
currentDF[, TRAIT] -0.062392 0.051283 -1.217 0.224459
Age -0.004974 0.005589 -0.890 0.374066
Gendermale 0.360477 0.108341 3.327 0.000958 ***
ORdate_year -0.235101 0.053086 -4.429 1.23e-05 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9702 on 400 degrees of freedom
Multiple R-squared: 0.07276, Adjusted R-squared: 0.06349
F-statistic: 7.848 on 4 and 400 DF, p-value: 4.264e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.062392
Standard error............: 0.051283
Odds ratio (effect size)..: 0.94
Lower 95% CI..............: 0.85
Upper 95% CI..............: 1.039
T-value...................: -1.216638
P-value...................: 0.2244592
R^2.......................: 0.072765
Adjusted r^2..............: 0.063493
Sample size of AE DB......: 2423
Sample size of model......: 405
Missing data %............: 83.28518
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
321.0628 0.2653 -0.1603
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2762 -0.6483 0.0060 0.6411 2.8309
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 308.664496 112.145327 2.752 0.00621 **
currentDF[, TRAIT] 0.009713 0.050789 0.191 0.84844
Age -0.006370 0.005821 -1.094 0.27459
Gendermale 0.277185 0.112881 2.456 0.01453 *
ORdate_year -0.153941 0.055991 -2.749 0.00627 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9721 on 365 degrees of freedom
Multiple R-squared: 0.03932, Adjusted R-squared: 0.02879
F-statistic: 3.734 on 4 and 365 DF, p-value: 0.005412
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TNFA_rank
Effect size...............: 0.009713
Standard error............: 0.050789
Odds ratio (effect size)..: 1.01
Lower 95% CI..............: 0.914
Upper 95% CI..............: 1.115
T-value...................: 0.191241
P-value...................: 0.8484435
R^2.......................: 0.039317
Adjusted r^2..............: 0.028789
Sample size of AE DB......: 2423
Sample size of model......: 370
Missing data %............: 84.72967
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
136.44181 0.34462 0.23501 -0.06817
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3632 -0.6092 -0.0177 0.6642 2.6913
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 133.066491 79.750174 1.669 0.09578 .
currentDF[, TRAIT] 0.342095 0.043437 7.876 1.81e-14 ***
Age -0.004360 0.004529 -0.963 0.33605
Gendermale 0.237429 0.086694 2.739 0.00637 **
ORdate_year -0.066341 0.039805 -1.667 0.09615 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9186 on 551 degrees of freedom
Multiple R-squared: 0.1617, Adjusted R-squared: 0.1557
F-statistic: 26.58 on 4 and 551 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.342095
Standard error............: 0.043437
Odds ratio (effect size)..: 1.408
Lower 95% CI..............: 1.293
Upper 95% CI..............: 1.533
T-value...................: 7.875623
P-value...................: 1.814862e-14
R^2.......................: 0.161741
Adjusted r^2..............: 0.155655
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing MCP1_rankattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year
-8.616e-15 1.000e+00 4.299e-18 essentially perfect fit: summary may be unreliable
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.867e-16 -1.743e-17 7.200e-19 1.902e-17 2.006e-16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.289e-15 3.874e-15 -2.140e+00 0.0328 *
currentDF[, TRAIT] 1.000e+00 2.124e-18 4.707e+17 <2e-16 ***
Age 2.370e-19 2.382e-19 9.950e-01 0.3201
Gendermale 1.815e-18 4.591e-18 3.950e-01 0.6927
ORdate_year 4.128e-18 1.934e-18 2.135e+00 0.0332 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 4.832e-17 on 551 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 5.94e+34 on 4 and 551 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MCP1_rank ' .essentially perfect fit: summary may be unreliableCollecting data.essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableWe have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 1
Standard error............: 0
Odds ratio (effect size)..: 2.718
Lower 95% CI..............: 2.718
Upper 95% CI..............: 2.718
T-value...................: 4.707378e+17
P-value...................: 0
R^2.......................: 1
Adjusted r^2..............: 1
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
474.0856 0.3358 0.2006 -0.2366
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.79686 -0.65092 -0.06877 0.58497 2.67954
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 468.332834 69.013614 6.786 3.22e-11 ***
currentDF[, TRAIT] 0.332995 0.038566 8.635 < 2e-16 ***
Age -0.005195 0.004459 -1.165 0.2446
Gendermale 0.204041 0.085614 2.383 0.0175 *
ORdate_year -0.233591 0.034451 -6.780 3.34e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8684 on 508 degrees of freedom
Multiple R-squared: 0.2051, Adjusted R-squared: 0.1988
F-statistic: 32.77 on 4 and 508 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.332995
Standard error............: 0.038566
Odds ratio (effect size)..: 1.395
Lower 95% CI..............: 1.294
Upper 95% CI..............: 1.505
T-value...................: 8.634514
P-value...................: 7.646036e-17
R^2.......................: 0.205089
Adjusted r^2..............: 0.19883
Sample size of AE DB......: 2423
Sample size of model......: 513
Missing data %............: 78.8279
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
202.9668 0.3373 0.2082 -0.1014
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2792 -0.5356 -0.0162 0.5823 3.0478
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 202.399940 76.702752 2.639 0.00856 **
currentDF[, TRAIT] 0.336096 0.042438 7.920 1.35e-14 ***
Age -0.001053 0.004542 -0.232 0.81680
Gendermale 0.208992 0.086714 2.410 0.01628 *
ORdate_year -0.101037 0.038283 -2.639 0.00855 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.912 on 544 degrees of freedom
Multiple R-squared: 0.1653, Adjusted R-squared: 0.1591
F-statistic: 26.93 on 4 and 544 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.336096
Standard error............: 0.042438
Odds ratio (effect size)..: 1.399
Lower 95% CI..............: 1.288
Upper 95% CI..............: 1.521
T-value...................: 7.919657
P-value...................: 1.348892e-14
R^2.......................: 0.165283
Adjusted r^2..............: 0.159146
Sample size of AE DB......: 2423
Sample size of model......: 549
Missing data %............: 77.34214
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
570.9520 0.2940 0.2282 -0.2850
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2468 -0.6400 -0.0431 0.6164 2.4929
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 564.938294 74.957612 7.537 2.04e-13 ***
currentDF[, TRAIT] 0.290517 0.041532 6.995 7.86e-12 ***
Age -0.003719 0.004609 -0.807 0.42009
Gendermale 0.230474 0.087474 2.635 0.00866 **
ORdate_year -0.281872 0.037422 -7.532 2.10e-13 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9197 on 541 degrees of freedom
Multiple R-squared: 0.1494, Adjusted R-squared: 0.1432
F-statistic: 23.76 on 4 and 541 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.290517
Standard error............: 0.041532
Odds ratio (effect size)..: 1.337
Lower 95% CI..............: 1.233
Upper 95% CI..............: 1.451
T-value...................: 6.995086
P-value...................: 7.859583e-12
R^2.......................: 0.149445
Adjusted r^2..............: 0.143156
Sample size of AE DB......: 2423
Sample size of model......: 546
Missing data %............: 77.46595
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
410.1428 0.4186 0.2370 -0.2047
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2812 -0.6240 -0.0607 0.5845 2.3274
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 408.108584 69.680872 5.857 8.64e-09 ***
currentDF[, TRAIT] 0.417366 0.038927 10.722 < 2e-16 ***
Age -0.002132 0.004490 -0.475 0.63514
Gendermale 0.238554 0.084533 2.822 0.00497 **
ORdate_year -0.203660 0.034782 -5.855 8.71e-09 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8559 on 492 degrees of freedom
Multiple R-squared: 0.2476, Adjusted R-squared: 0.2415
F-statistic: 40.48 on 4 and 492 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.417366
Standard error............: 0.038927
Odds ratio (effect size)..: 1.518
Lower 95% CI..............: 1.406
Upper 95% CI..............: 1.638
T-value...................: 10.72171
P-value...................: 3.021844e-24
R^2.......................: 0.247632
Adjusted r^2..............: 0.241515
Sample size of AE DB......: 2423
Sample size of model......: 497
Missing data %............: 79.48824
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
480.4459 0.3284 0.1907 -0.2398
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4214 -0.6698 -0.0780 0.6000 2.5515
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 474.710009 71.895573 6.603 9.51e-11 ***
currentDF[, TRAIT] 0.326133 0.039426 8.272 9.95e-16 ***
Age -0.004255 0.004505 -0.944 0.345
Gendermale 0.193342 0.086639 2.232 0.026 *
ORdate_year -0.236820 0.035891 -6.598 9.78e-11 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9139 on 551 degrees of freedom
Multiple R-squared: 0.1704, Adjusted R-squared: 0.1644
F-statistic: 28.29 on 4 and 551 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.326133
Standard error............: 0.039426
Odds ratio (effect size)..: 1.386
Lower 95% CI..............: 1.283
Upper 95% CI..............: 1.497
T-value...................: 8.272003
P-value...................: 9.945147e-16
R^2.......................: 0.170403
Adjusted r^2..............: 0.16438
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
244.2877 0.2704 0.1328 -0.1220
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.95046 -0.61035 -0.07473 0.62130 2.69438
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 240.766099 89.076405 2.703 0.00712 **
currentDF[, TRAIT] 0.267944 0.044061 6.081 2.45e-09 ***
Age -0.002735 0.004820 -0.567 0.57068
Gendermale 0.133451 0.093203 1.432 0.15285
ORdate_year -0.120119 0.044459 -2.702 0.00714 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.922 on 476 degrees of freedom
Multiple R-squared: 0.1124, Adjusted R-squared: 0.1049
F-statistic: 15.07 on 4 and 476 DF, p-value: 1.324e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.267944
Standard error............: 0.044061
Odds ratio (effect size)..: 1.307
Lower 95% CI..............: 1.199
Upper 95% CI..............: 1.425
T-value...................: 6.08117
P-value...................: 2.451314e-09
R^2.......................: 0.112375
Adjusted r^2..............: 0.104915
Sample size of AE DB......: 2423
Sample size of model......: 481
Missing data %............: 80.14858
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
162.585286 0.425915 -0.006632 0.277017 -0.081011
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.11016 -0.55126 -0.02245 0.58456 2.17519
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 162.585286 72.281906 2.249 0.024886 *
currentDF[, TRAIT] 0.425915 0.039168 10.874 < 2e-16 ***
Age -0.006632 0.004326 -1.533 0.125889
Gendermale 0.277017 0.082886 3.342 0.000888 ***
ORdate_year -0.081011 0.036082 -2.245 0.025152 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8792 on 551 degrees of freedom
Multiple R-squared: 0.2322, Adjusted R-squared: 0.2266
F-statistic: 41.65 on 4 and 551 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.425915
Standard error............: 0.039168
Odds ratio (effect size)..: 1.531
Lower 95% CI..............: 1.418
Upper 95% CI..............: 1.653
T-value...................: 10.87395
P-value...................: 4.394713e-25
R^2.......................: 0.232156
Adjusted r^2..............: 0.226582
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
253.6058 0.3792 0.2858 -0.1266
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.9082 -0.6351 -0.0943 0.5437 3.2873
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 250.003830 72.176641 3.464 0.000578 ***
currentDF[, TRAIT] 0.377012 0.040358 9.342 < 2e-16 ***
Age -0.004058 0.004424 -0.917 0.359527
Gendermale 0.288455 0.084892 3.398 0.000733 ***
ORdate_year -0.124711 0.036026 -3.462 0.000583 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8631 on 503 degrees of freedom
Multiple R-squared: 0.2225, Adjusted R-squared: 0.2163
F-statistic: 35.99 on 4 and 503 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.377012
Standard error............: 0.040358
Odds ratio (effect size)..: 1.458
Lower 95% CI..............: 1.347
Upper 95% CI..............: 1.578
T-value...................: 9.341649
P-value...................: 3.06886e-19
R^2.......................: 0.222496
Adjusted r^2..............: 0.216313
Sample size of AE DB......: 2423
Sample size of model......: 508
Missing data %............: 79.03426
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
440.2481 0.5843 0.1648 -0.2198
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6155 -0.5348 -0.0631 0.4750 2.5142
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 438.288904 60.992665 7.186 2.18e-12 ***
currentDF[, TRAIT] 0.583484 0.033999 17.162 < 2e-16 ***
Age -0.001501 0.003857 -0.389 0.6972
Gendermale 0.165816 0.073853 2.245 0.0252 *
ORdate_year -0.218731 0.030448 -7.184 2.22e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7796 on 550 degrees of freedom
Multiple R-squared: 0.3925, Adjusted R-squared: 0.3881
F-statistic: 88.84 on 4 and 550 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.583484
Standard error............: 0.033999
Odds ratio (effect size)..: 1.792
Lower 95% CI..............: 1.677
Upper 95% CI..............: 1.916
T-value...................: 17.16184
P-value...................: 3.464325e-53
R^2.......................: 0.392512
Adjusted r^2..............: 0.388094
Sample size of AE DB......: 2423
Sample size of model......: 555
Missing data %............: 77.09451
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
100.25480 0.65142 0.23469 -0.05012
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.12956 -0.37673 0.03337 0.42560 2.24890
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 101.612492 59.566339 1.706 0.088596 .
currentDF[, TRAIT] 0.653254 0.032594 20.042 < 2e-16 ***
Age 0.001807 0.003650 0.495 0.620785
Gendermale 0.233698 0.069490 3.363 0.000824 ***
ORdate_year -0.050855 0.029732 -1.710 0.087739 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7369 on 551 degrees of freedom
Multiple R-squared: 0.4606, Adjusted R-squared: 0.4567
F-statistic: 117.6 on 4 and 551 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.653254
Standard error............: 0.032594
Odds ratio (effect size)..: 1.922
Lower 95% CI..............: 1.803
Upper 95% CI..............: 2.049
T-value...................: 20.042
P-value...................: 1.602312e-67
R^2.......................: 0.460602
Adjusted r^2..............: 0.456687
Sample size of AE DB......: 2423
Sample size of model......: 556
Missing data %............: 77.05324
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
636.0065 0.3152 0.2101 -0.3175
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2726 -0.6299 -0.0513 0.6024 2.7533
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 627.435812 87.528538 7.168 3.16e-12 ***
currentDF[, TRAIT] 0.316635 0.046349 6.832 2.76e-11 ***
Age -0.005637 0.004958 -1.137 0.2562
Gendermale 0.209258 0.096821 2.161 0.0312 *
ORdate_year -0.312989 0.043693 -7.163 3.26e-12 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9106 on 447 degrees of freedom
Multiple R-squared: 0.1538, Adjusted R-squared: 0.1463
F-statistic: 20.32 on 4 and 447 DF, p-value: 2.158e-15
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.316635
Standard error............: 0.046349
Odds ratio (effect size)..: 1.373
Lower 95% CI..............: 1.253
Upper 95% CI..............: 1.503
T-value...................: 6.831545
P-value...................: 2.757679e-11
R^2.......................: 0.153843
Adjusted r^2..............: 0.146271
Sample size of AE DB......: 2423
Sample size of model......: 452
Missing data %............: 81.34544
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
370.4817 0.1160 0.2732 -0.1850
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4713 -0.6428 -0.0446 0.6781 2.6902
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 366.409129 78.009902 4.697 3.38e-06 ***
currentDF[, TRAIT] 0.114467 0.042999 2.662 0.00800 **
Age -0.003384 0.004812 -0.703 0.48216
Gendermale 0.274764 0.092739 2.963 0.00319 **
ORdate_year -0.182825 0.038942 -4.695 3.41e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9567 on 523 degrees of freedom
Multiple R-squared: 0.07951, Adjusted R-squared: 0.07247
F-statistic: 11.29 on 4 and 523 DF, p-value: 8.501e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.114467
Standard error............: 0.042999
Odds ratio (effect size)..: 1.121
Lower 95% CI..............: 1.031
Upper 95% CI..............: 1.22
T-value...................: 2.662105
P-value...................: 0.008004355
R^2.......................: 0.079508
Adjusted r^2..............: 0.072468
Sample size of AE DB......: 2423
Sample size of model......: 528
Missing data %............: 78.20883
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
289.5407 0.3330 0.3622 -0.1446
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4835 -0.5786 0.0060 0.5934 2.6531
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 284.458423 77.752159 3.659 0.000279 ***
currentDF[, TRAIT] 0.330774 0.040358 8.196 1.91e-15 ***
Age -0.005165 0.004588 -1.126 0.260745
Gendermale 0.364122 0.088326 4.122 4.35e-05 ***
ORdate_year -0.141897 0.038812 -3.656 0.000282 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9102 on 526 degrees of freedom
Multiple R-squared: 0.1664, Adjusted R-squared: 0.16
F-statistic: 26.24 on 4 and 526 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.330774
Standard error............: 0.040358
Odds ratio (effect size)..: 1.392
Lower 95% CI..............: 1.286
Upper 95% CI..............: 1.507
T-value...................: 8.196056
P-value...................: 1.905934e-15
R^2.......................: 0.166354
Adjusted r^2..............: 0.160015
Sample size of AE DB......: 2423
Sample size of model......: 531
Missing data %............: 78.08502
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
389.774698 0.432719 -0.007878 0.143507 -0.194289
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2706 -0.4946 0.0294 0.5473 2.8701
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 389.774698 73.252460 5.321 1.53e-07 ***
currentDF[, TRAIT] 0.432719 0.038141 11.345 < 2e-16 ***
Age -0.007878 0.004362 -1.806 0.0715 .
Gendermale 0.143507 0.084864 1.691 0.0914 .
ORdate_year -0.194289 0.036567 -5.313 1.59e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8664 on 526 degrees of freedom
Multiple R-squared: 0.2447, Adjusted R-squared: 0.239
F-statistic: 42.61 on 4 and 526 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.432719
Standard error............: 0.038141
Odds ratio (effect size)..: 1.541
Lower 95% CI..............: 1.43
Upper 95% CI..............: 1.661
T-value...................: 11.34524
P-value...................: 7.740327e-27
R^2.......................: 0.244712
Adjusted r^2..............: 0.238968
Sample size of AE DB......: 2423
Sample size of model......: 531
Missing data %............: 78.08502
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
257.60136 0.56444 -0.01056 0.20596 -0.12826
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1884 -0.5048 -0.0073 0.5197 2.8723
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 257.601363 67.289335 3.828 0.000145 ***
currentDF[, TRAIT] 0.564438 0.035147 16.059 < 2e-16 ***
Age -0.010561 0.003989 -2.648 0.008352 **
Gendermale 0.205960 0.076739 2.684 0.007507 **
ORdate_year -0.128262 0.033591 -3.818 0.000150 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7912 on 525 degrees of freedom
Multiple R-squared: 0.3698, Adjusted R-squared: 0.365
F-statistic: 77.03 on 4 and 525 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.564438
Standard error............: 0.035147
Odds ratio (effect size)..: 1.758
Lower 95% CI..............: 1.641
Upper 95% CI..............: 1.884
T-value...................: 16.05932
P-value...................: 1.678039e-47
R^2.......................: 0.369827
Adjusted r^2..............: 0.365025
Sample size of AE DB......: 2423
Sample size of model......: 530
Missing data %............: 78.12629
Analysis of MCP1_plasma_olink_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
0.05528 -0.19739
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.80410 -0.55564 -0.04765 0.54464 2.42405
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -98.56119 198.43504 -0.497 0.6204
currentDF[, TRAIT] -0.18207 0.09437 -1.929 0.0564 .
Age 0.01002 0.01048 0.956 0.3412
Gendermale 0.03336 0.21175 0.158 0.8751
ORdate_year 0.04887 0.09908 0.493 0.6228
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9113 on 105 degrees of freedom
Multiple R-squared: 0.05423, Adjusted R-squared: 0.0182
F-statistic: 1.505 on 4 and 105 DF, p-value: 0.206
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.182074
Standard error............: 0.094369
Odds ratio (effect size)..: 0.834
Lower 95% CI..............: 0.693
Upper 95% CI..............: 1.003
T-value...................: -1.929381
P-value...................: 0.05638267
R^2.......................: 0.054227
Adjusted r^2..............: 0.018198
Sample size of AE DB......: 2423
Sample size of model......: 110
Missing data %............: 95.46017
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.98595 0.01474
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.93797 -0.58716 0.03406 0.53163 2.30401
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 77.28843 189.39696 0.408 0.684
currentDF[, TRAIT] -0.11422 0.09109 -1.254 0.213
Age 0.01469 0.01014 1.449 0.150
Gendermale -0.03156 0.21832 -0.145 0.885
ORdate_year -0.03906 0.09458 -0.413 0.680
Residual standard error: 0.8822 on 105 degrees of freedom
Multiple R-squared: 0.03714, Adjusted R-squared: 0.0004561
F-statistic: 1.012 on 4 and 105 DF, p-value: 0.4045
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.114223
Standard error............: 0.091088
Odds ratio (effect size)..: 0.892
Lower 95% CI..............: 0.746
Upper 95% CI..............: 1.066
T-value...................: -1.253978
P-value...................: 0.2126348
R^2.......................: 0.037137
Adjusted r^2..............: 0.000456
Sample size of AE DB......: 2423
Sample size of model......: 110
Missing data %............: 95.46017
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
0.05697 -0.21970
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.79317 -0.49890 -0.04681 0.54990 2.43833
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -37.019296 188.485386 -0.196 0.8447
currentDF[, TRAIT] -0.211065 0.088773 -2.378 0.0192 *
Age 0.008552 0.010139 0.843 0.4008
Gendermale 0.104685 0.196926 0.532 0.5961
ORdate_year 0.018178 0.094140 0.193 0.8472
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8917 on 110 degrees of freedom
Multiple R-squared: 0.06696, Adjusted R-squared: 0.03304
F-statistic: 1.974 on 4 and 110 DF, p-value: 0.1035
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.211065
Standard error............: 0.088773
Odds ratio (effect size)..: 0.81
Lower 95% CI..............: 0.68
Upper 95% CI..............: 0.964
T-value...................: -2.377576
P-value...................: 0.01915275
R^2.......................: 0.066965
Adjusted r^2..............: 0.033036
Sample size of AE DB......: 2423
Sample size of model......: 115
Missing data %............: 95.25382
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)
Coefficients:
(Intercept) Gendermale
-0.1223 0.2984
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.88936 -0.52722 -0.07808 0.51187 2.19902
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -109.14464 177.17402 -0.616 0.539
currentDF[, TRAIT] -0.04185 0.09155 -0.457 0.649
Age 0.00721 0.01012 0.713 0.478
Gendermale 0.23549 0.19754 1.192 0.236
ORdate_year 0.05420 0.08848 0.613 0.541
Residual standard error: 0.8921 on 111 degrees of freedom
Multiple R-squared: 0.03189, Adjusted R-squared: -0.002996
F-statistic: 0.9141 on 4 and 111 DF, p-value: 0.4584
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL6_rank
Effect size...............: -0.041845
Standard error............: 0.091549
Odds ratio (effect size)..: 0.959
Lower 95% CI..............: 0.801
Upper 95% CI..............: 1.148
T-value...................: -0.457074
P-value...................: 0.6485114
R^2.......................: 0.031891
Adjusted r^2..............: -0.002996
Sample size of AE DB......: 2423
Sample size of model......: 116
Missing data %............: 95.21255
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender, data = currentDF)
Coefficients:
(Intercept) Gendermale
-0.1465 0.3081
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.95382 -0.54688 -0.03079 0.54984 2.04549
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -84.760726 164.801599 -0.514 0.608
currentDF[, TRAIT] -0.043833 0.084563 -0.518 0.605
Age 0.009361 0.009983 0.938 0.350
Gendermale 0.275385 0.192464 1.431 0.155
ORdate_year 0.041924 0.082298 0.509 0.611
Residual standard error: 0.8994 on 109 degrees of freedom
Multiple R-squared: 0.03728, Adjusted R-squared: 0.001953
F-statistic: 1.055 on 4 and 109 DF, p-value: 0.3823
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL8_rank
Effect size...............: -0.043833
Standard error............: 0.084563
Odds ratio (effect size)..: 0.957
Lower 95% CI..............: 0.811
Upper 95% CI..............: 1.13
T-value...................: -0.518354
P-value...................: 0.6052623
R^2.......................: 0.037282
Adjusted r^2..............: 0.001953
Sample size of AE DB......: 2423
Sample size of model......: 114
Missing data %............: 95.29509
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.80613 0.01346
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.90051 -0.55683 -0.06317 0.56491 2.17186
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -84.354215 137.937716 -0.612 0.542
currentDF[, TRAIT] -0.020511 0.095087 -0.216 0.830
Age 0.012054 0.009766 1.234 0.219
Gendermale 0.112250 0.181989 0.617 0.539
ORdate_year 0.041702 0.068827 0.606 0.546
Residual standard error: 0.9303 on 124 degrees of freedom
Multiple R-squared: 0.02176, Adjusted R-squared: -0.009793
F-statistic: 0.6897 on 4 and 124 DF, p-value: 0.6004
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL9_rank
Effect size...............: -0.020511
Standard error............: 0.095087
Odds ratio (effect size)..: 0.98
Lower 95% CI..............: 0.813
Upper 95% CI..............: 1.18
T-value...................: -0.215704
P-value...................: 0.8295729
R^2.......................: 0.021763
Adjusted r^2..............: -0.009793
Sample size of AE DB......: 2423
Sample size of model......: 129
Missing data %............: 94.67602
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-1.17636 0.01715
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.91227 -0.53237 0.03727 0.50957 2.25940
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 100.75126 203.28517 0.496 0.621
currentDF[, TRAIT] -0.09617 0.09151 -1.051 0.296
Age 0.01518 0.01003 1.514 0.133
Gendermale 0.05430 0.20690 0.262 0.794
ORdate_year -0.05084 0.10150 -0.501 0.618
Residual standard error: 0.8517 on 99 degrees of freedom
Multiple R-squared: 0.04456, Adjusted R-squared: 0.005956
F-statistic: 1.154 on 4 and 99 DF, p-value: 0.3357
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.096166
Standard error............: 0.091509
Odds ratio (effect size)..: 0.908
Lower 95% CI..............: 0.759
Upper 95% CI..............: 1.087
T-value...................: -1.050892
P-value...................: 0.2958665
R^2.......................: 0.044559
Adjusted r^2..............: 0.005956
Sample size of AE DB......: 2423
Sample size of model......: 104
Missing data %............: 95.7078
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
0.04891 -0.16078
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.82530 -0.46872 -0.03063 0.52189 2.35803
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -80.22379 204.07213 -0.393 0.695
currentDF[, TRAIT] -0.13077 0.09636 -1.357 0.178
Age 0.01076 0.01048 1.027 0.307
Gendermale 0.09355 0.20304 0.461 0.646
ORdate_year 0.03967 0.10189 0.389 0.698
Residual standard error: 0.8882 on 101 degrees of freedom
Multiple R-squared: 0.04647, Adjusted R-squared: 0.008702
F-statistic: 1.23 on 4 and 101 DF, p-value: 0.3027
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.130774
Standard error............: 0.096362
Odds ratio (effect size)..: 0.877
Lower 95% CI..............: 0.726
Upper 95% CI..............: 1.06
T-value...................: -1.357114
P-value...................: 0.17777
R^2.......................: 0.046465
Adjusted r^2..............: 0.008702
Sample size of AE DB......: 2423
Sample size of model......: 106
Missing data %............: 95.62526
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.9757 0.0158
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.93265 -0.56828 -0.03252 0.53971 2.15892
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -86.087160 133.757602 -0.644 0.521
currentDF[, TRAIT] -0.003773 0.081367 -0.046 0.963
Age 0.014494 0.008972 1.616 0.108
Gendermale 0.122504 0.169044 0.725 0.470
ORdate_year 0.042476 0.066752 0.636 0.526
Residual standard error: 0.9191 on 141 degrees of freedom
Multiple R-squared: 0.02831, Adjusted R-squared: 0.0007401
F-statistic: 1.027 on 4 and 141 DF, p-value: 0.3957
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL13_rank
Effect size...............: -0.003773
Standard error............: 0.081367
Odds ratio (effect size)..: 0.996
Lower 95% CI..............: 0.849
Upper 95% CI..............: 1.168
T-value...................: -0.046365
P-value...................: 0.9630847
R^2.......................: 0.028306
Adjusted r^2..............: 0.00074
Sample size of AE DB......: 2423
Sample size of model......: 146
Missing data %............: 93.97441
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.9757 0.0158
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.95077 -0.58542 -0.03054 0.55228 2.16410
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -87.09748 133.81592 -0.651 0.516
currentDF[, TRAIT] 0.01675 0.07877 0.213 0.832
Age 0.01478 0.00897 1.648 0.102
Gendermale 0.12003 0.16908 0.710 0.479
ORdate_year 0.04297 0.06678 0.643 0.521
Residual standard error: 0.9189 on 141 degrees of freedom
Multiple R-squared: 0.0286, Adjusted R-squared: 0.001045
F-statistic: 1.038 on 4 and 141 DF, p-value: 0.3899
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.016751
Standard error............: 0.07877
Odds ratio (effect size)..: 1.017
Lower 95% CI..............: 0.871
Upper 95% CI..............: 1.187
T-value...................: 0.212662
P-value...................: 0.831898
R^2.......................: 0.028603
Adjusted r^2..............: 0.001045
Sample size of AE DB......: 2423
Sample size of model......: 146
Missing data %............: 93.97441
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age
-0.81875 -0.18167 0.01254
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.87020 -0.45372 -0.02656 0.55347 2.34872
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 69.977349 185.972462 0.376 0.7074
currentDF[, TRAIT] -0.193637 0.099292 -1.950 0.0537 .
Age 0.011590 0.009197 1.260 0.2103
Gendermale 0.096676 0.189724 0.510 0.6114
ORdate_year -0.035341 0.092829 -0.381 0.7042
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8616 on 108 degrees of freedom
Multiple R-squared: 0.06484, Adjusted R-squared: 0.03021
F-statistic: 1.872 on 4 and 108 DF, p-value: 0.1205
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.193637
Standard error............: 0.099292
Odds ratio (effect size)..: 0.824
Lower 95% CI..............: 0.678
Upper 95% CI..............: 1.001
T-value...................: -1.950178
P-value...................: 0.05374676
R^2.......................: 0.064845
Adjusted r^2..............: 0.030209
Sample size of AE DB......: 2423
Sample size of model......: 113
Missing data %............: 95.33636
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.84412 0.01299
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.94671 -0.49633 0.02807 0.47166 2.31411
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 91.268676 181.079974 0.504 0.615
currentDF[, TRAIT] -0.135081 0.096048 -1.406 0.163
Age 0.014083 0.009820 1.434 0.155
Gendermale -0.002892 0.194439 -0.015 0.988
ORdate_year -0.046018 0.090452 -0.509 0.612
Residual standard error: 0.8512 on 102 degrees of freedom
Multiple R-squared: 0.03893, Adjusted R-squared: 0.001243
F-statistic: 1.033 on 4 and 102 DF, p-value: 0.394
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: TNFA_rank
Effect size...............: -0.135081
Standard error............: 0.096048
Odds ratio (effect size)..: 0.874
Lower 95% CI..............: 0.724
Upper 95% CI..............: 1.055
T-value...................: -1.406393
P-value...................: 0.1626467
R^2.......................: 0.038932
Adjusted r^2..............: 0.001243
Sample size of AE DB......: 2423
Sample size of model......: 107
Missing data %............: 95.58399
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.9757 0.0158
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.93552 -0.56964 -0.02567 0.56116 2.23417
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.171e+02 1.471e+02 -0.796 0.427
currentDF[, TRAIT] 4.489e-02 8.848e-02 0.507 0.613
Age 1.473e-02 8.903e-03 1.655 0.100
Gendermale 1.132e-01 1.696e-01 0.667 0.506
ORdate_year 5.796e-02 7.338e-02 0.790 0.431
Residual standard error: 0.9183 on 141 degrees of freedom
Multiple R-squared: 0.03006, Adjusted R-squared: 0.002546
F-statistic: 1.093 on 4 and 141 DF, p-value: 0.3627
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.044889
Standard error............: 0.088477
Odds ratio (effect size)..: 1.046
Lower 95% CI..............: 0.879
Upper 95% CI..............: 1.244
T-value...................: 0.507352
P-value...................: 0.6127002
R^2.......................: 0.030062
Adjusted r^2..............: 0.002546
Sample size of AE DB......: 2423
Sample size of model......: 146
Missing data %............: 93.97441
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.89713 0.01467
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.92614 -0.58024 -0.02805 0.54201 2.07630
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -45.588589 137.883019 -0.331 0.741
currentDF[, TRAIT] -0.078501 0.078682 -0.998 0.320
Age 0.013586 0.008911 1.525 0.130
Gendermale 0.090832 0.170115 0.534 0.594
ORdate_year 0.022311 0.068810 0.324 0.746
Residual standard error: 0.9099 on 138 degrees of freedom
Multiple R-squared: 0.03021, Adjusted R-squared: 0.002102
F-statistic: 1.075 on 4 and 138 DF, p-value: 0.3714
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MCP1_rank
Effect size...............: -0.078501
Standard error............: 0.078682
Odds ratio (effect size)..: 0.925
Lower 95% CI..............: 0.792
Upper 95% CI..............: 1.079
T-value...................: -0.997697
P-value...................: 0.3201726
R^2.......................: 0.030212
Adjusted r^2..............: 0.002102
Sample size of AE DB......: 2423
Sample size of model......: 143
Missing data %............: 94.09822
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.83790 0.01399
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.95848 -0.60412 -0.04601 0.56454 2.14989
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -93.275622 137.003149 -0.681 0.497
currentDF[, TRAIT] 0.031257 0.088128 0.355 0.723
Age 0.013330 0.009685 1.376 0.171
Gendermale 0.110494 0.181567 0.609 0.544
ORdate_year 0.046115 0.068363 0.675 0.501
Residual standard error: 0.9273 on 125 degrees of freedom
Multiple R-squared: 0.02401, Adjusted R-squared: -0.007225
F-statistic: 0.7687 on 4 and 125 DF, p-value: 0.5476
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.031257
Standard error............: 0.088128
Odds ratio (effect size)..: 1.032
Lower 95% CI..............: 0.868
Upper 95% CI..............: 1.226
T-value...................: 0.354674
P-value...................: 0.7234312
R^2.......................: 0.024007
Adjusted r^2..............: -0.007225
Sample size of AE DB......: 2423
Sample size of model......: 130
Missing data %............: 94.63475
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.98348 0.01604
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.01523 -0.57039 -0.02453 0.56805 2.12679
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.045e+02 1.516e+02 -0.690 0.4916
currentDF[, TRAIT] 4.052e-02 8.985e-02 0.451 0.6527
Age 1.566e-02 8.906e-03 1.759 0.0808 .
Gendermale 7.603e-02 1.696e-01 0.448 0.6547
ORdate_year 5.166e-02 7.563e-02 0.683 0.4957
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9135 on 139 degrees of freedom
Multiple R-squared: 0.02827, Adjusted R-squared: 0.0003107
F-statistic: 1.011 on 4 and 139 DF, p-value: 0.4039
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.040516
Standard error............: 0.089846
Odds ratio (effect size)..: 1.041
Lower 95% CI..............: 0.873
Upper 95% CI..............: 1.242
T-value...................: 0.450953
P-value...................: 0.6527264
R^2.......................: 0.028274
Adjusted r^2..............: 0.000311
Sample size of AE DB......: 2423
Sample size of model......: 144
Missing data %............: 94.05695
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.99133 0.01608
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.95399 -0.58629 -0.04939 0.53996 2.15950
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -87.160521 138.078970 -0.631 0.5289
currentDF[, TRAIT] 0.013616 0.082156 0.166 0.8686
Age 0.015029 0.008992 1.671 0.0969 .
Gendermale 0.126103 0.169209 0.745 0.4574
ORdate_year 0.042996 0.068917 0.624 0.5337
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9142 on 138 degrees of freedom
Multiple R-squared: 0.0309, Adjusted R-squared: 0.002807
F-statistic: 1.1 on 4 and 138 DF, p-value: 0.3592
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.013616
Standard error............: 0.082156
Odds ratio (effect size)..: 1.014
Lower 95% CI..............: 0.863
Upper 95% CI..............: 1.191
T-value...................: 0.165736
P-value...................: 0.8686069
R^2.......................: 0.030897
Adjusted r^2..............: 0.002807
Sample size of AE DB......: 2423
Sample size of model......: 143
Missing data %............: 94.09822
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.94460 0.01541
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.8231 -0.6151 -0.0713 0.5332 2.0075
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -41.584262 132.129013 -0.315 0.753
currentDF[, TRAIT] -0.105148 0.088400 -1.189 0.236
Age 0.012380 0.009309 1.330 0.186
Gendermale 0.138027 0.172064 0.802 0.424
ORdate_year 0.020328 0.065933 0.308 0.758
Residual standard error: 0.8917 on 129 degrees of freedom
Multiple R-squared: 0.03778, Adjusted R-squared: 0.007941
F-statistic: 1.266 on 4 and 129 DF, p-value: 0.2867
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IP10_rank
Effect size...............: -0.105148
Standard error............: 0.0884
Odds ratio (effect size)..: 0.9
Lower 95% CI..............: 0.757
Upper 95% CI..............: 1.07
T-value...................: -1.189457
P-value...................: 0.2364446
R^2.......................: 0.037777
Adjusted r^2..............: 0.007941
Sample size of AE DB......: 2423
Sample size of model......: 134
Missing data %............: 94.46967
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.9757 0.0158
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.99388 -0.58596 -0.00204 0.57674 2.12689
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -81.644134 133.691722 -0.611 0.5424
currentDF[, TRAIT] 0.050495 0.077292 0.653 0.5146
Age 0.015216 0.008949 1.700 0.0913 .
Gendermale 0.112468 0.169219 0.665 0.5074
ORdate_year 0.040242 0.066720 0.603 0.5474
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9177 on 141 degrees of freedom
Multiple R-squared: 0.03122, Adjusted R-squared: 0.003741
F-statistic: 1.136 on 4 and 141 DF, p-value: 0.342
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.050495
Standard error............: 0.077292
Odds ratio (effect size)..: 1.052
Lower 95% CI..............: 0.904
Upper 95% CI..............: 1.224
T-value...................: 0.653303
P-value...................: 0.5146247
R^2.......................: 0.031224
Adjusted r^2..............: 0.003741
Sample size of AE DB......: 2423
Sample size of model......: 146
Missing data %............: 93.97441
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-1.02529 0.01661
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.97835 -0.61013 -0.07078 0.51238 2.18088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.717e+02 1.668e+02 -1.029 0.3054
currentDF[, TRAIT] 8.062e-02 9.722e-02 0.829 0.4087
Age 1.719e-02 9.547e-03 1.800 0.0744 .
Gendermale 9.455e-02 1.832e-01 0.516 0.6068
ORdate_year 8.510e-02 8.320e-02 1.023 0.3085
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9151 on 118 degrees of freedom
Multiple R-squared: 0.03897, Adjusted R-squared: 0.006396
F-statistic: 1.196 on 4 and 118 DF, p-value: 0.3161
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.080616
Standard error............: 0.097221
Odds ratio (effect size)..: 1.084
Lower 95% CI..............: 0.896
Upper 95% CI..............: 1.311
T-value...................: 0.829208
P-value...................: 0.4086608
R^2.......................: 0.038973
Adjusted r^2..............: 0.006396
Sample size of AE DB......: 2423
Sample size of model......: 123
Missing data %............: 94.92365
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.9757 0.0158
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.92628 -0.57922 -0.02765 0.54149 2.15029
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -81.424710 139.057611 -0.586 0.559
currentDF[, TRAIT] -0.010309 0.086714 -0.119 0.906
Age 0.014569 0.008905 1.636 0.104
Gendermale 0.121662 0.168863 0.720 0.472
ORdate_year 0.040149 0.069395 0.579 0.564
Residual standard error: 0.919 on 141 degrees of freedom
Multiple R-squared: 0.02839, Adjusted R-squared: 0.000825
F-statistic: 1.03 on 4 and 141 DF, p-value: 0.3941
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: PARC_rank
Effect size...............: -0.010309
Standard error............: 0.086714
Odds ratio (effect size)..: 0.99
Lower 95% CI..............: 0.835
Upper 95% CI..............: 1.173
T-value...................: -0.11889
P-value...................: 0.9055319
R^2.......................: 0.028388
Adjusted r^2..............: 0.000825
Sample size of AE DB......: 2423
Sample size of model......: 146
Missing data %............: 93.97441
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.87789 0.01455
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.89502 -0.56030 -0.06066 0.55027 2.17650
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -78.260055 149.602863 -0.523 0.602
currentDF[, TRAIT] -0.017447 0.088505 -0.197 0.844
Age 0.013075 0.009337 1.400 0.164
Gendermale 0.116296 0.179872 0.647 0.519
ORdate_year 0.038627 0.074653 0.517 0.606
Residual standard error: 0.924 on 126 degrees of freedom
Multiple R-squared: 0.02608, Adjusted R-squared: -0.004842
F-statistic: 0.8434 on 4 and 126 DF, p-value: 0.5002
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MDC_rank
Effect size...............: -0.017447
Standard error............: 0.088505
Odds ratio (effect size)..: 0.983
Lower 95% CI..............: 0.826
Upper 95% CI..............: 1.169
T-value...................: -0.197132
P-value...................: 0.8440421
R^2.......................: 0.026076
Adjusted r^2..............: -0.004842
Sample size of AE DB......: 2423
Sample size of model......: 131
Missing data %............: 94.59348
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.9757 0.0158
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.9582 -0.5655 -0.0575 0.5314 2.0616
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -92.963897 133.298364 -0.697 0.487
currentDF[, TRAIT] -0.087945 0.079177 -1.111 0.269
Age 0.013841 0.008887 1.557 0.122
Gendermale 0.134212 0.168449 0.797 0.427
ORdate_year 0.045927 0.066523 0.690 0.491
Residual standard error: 0.9151 on 141 degrees of freedom
Multiple R-squared: 0.03672, Adjusted R-squared: 0.009392
F-statistic: 1.344 on 4 and 141 DF, p-value: 0.2567
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: OPG_rank
Effect size...............: -0.087945
Standard error............: 0.079177
Odds ratio (effect size)..: 0.916
Lower 95% CI..............: 0.784
Upper 95% CI..............: 1.07
T-value...................: -1.11073
P-value...................: 0.2685751
R^2.......................: 0.03672
Adjusted r^2..............: 0.009392
Sample size of AE DB......: 2423
Sample size of model......: 146
Missing data %............: 93.97441
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.9757 0.0158
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.94414 -0.57229 -0.03489 0.55021 2.16904
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -90.136638 142.199328 -0.634 0.527
currentDF[, TRAIT] 0.007033 0.081408 0.086 0.931
Age 0.014616 0.008941 1.635 0.104
Gendermale 0.122846 0.169041 0.727 0.469
ORdate_year 0.044493 0.070953 0.627 0.532
Residual standard error: 0.9191 on 141 degrees of freedom
Multiple R-squared: 0.02834, Adjusted R-squared: 0.0007777
F-statistic: 1.028 on 4 and 141 DF, p-value: 0.3949
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.007033
Standard error............: 0.081408
Odds ratio (effect size)..: 1.007
Lower 95% CI..............: 0.859
Upper 95% CI..............: 1.181
T-value...................: 0.086389
P-value...................: 0.9312801
R^2.......................: 0.028342
Adjusted r^2..............: 0.000778
Sample size of AE DB......: 2423
Sample size of model......: 146
Missing data %............: 93.97441
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age
-1.38843 0.14101 0.02083
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.90531 -0.49521 -0.06886 0.55662 2.11034
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -69.155745 155.557252 -0.445 0.6574
currentDF[, TRAIT] 0.141825 0.095916 1.479 0.1419
Age 0.019010 0.008747 2.173 0.0318 *
Gendermale 0.222823 0.170217 1.309 0.1931
ORdate_year 0.033805 0.077639 0.435 0.6641
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8513 on 118 degrees of freedom
Multiple R-squared: 0.08103, Adjusted R-squared: 0.04988
F-statistic: 2.601 on 4 and 118 DF, p-value: 0.03951
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.141825
Standard error............: 0.095916
Odds ratio (effect size)..: 1.152
Lower 95% CI..............: 0.955
Upper 95% CI..............: 1.391
T-value...................: 1.47864
P-value...................: 0.141901
R^2.......................: 0.081031
Adjusted r^2..............: 0.04988
Sample size of AE DB......: 2423
Sample size of model......: 123
Missing data %............: 94.92365
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-1.08985 0.01801
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.06707 -0.56340 -0.00328 0.50644 2.02584
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -94.194595 134.147807 -0.702 0.4837
currentDF[, TRAIT] 0.075710 0.080572 0.940 0.3490
Age 0.016000 0.008837 1.810 0.0724 .
Gendermale 0.166370 0.166292 1.000 0.3188
ORdate_year 0.046473 0.066949 0.694 0.4887
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9166 on 141 degrees of freedom
Multiple R-squared: 0.04313, Adjusted R-squared: 0.01599
F-statistic: 1.589 on 4 and 141 DF, p-value: 0.1805
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.07571
Standard error............: 0.080572
Odds ratio (effect size)..: 1.079
Lower 95% CI..............: 0.921
Upper 95% CI..............: 1.263
T-value...................: 0.939664
P-value...................: 0.3489971
R^2.......................: 0.043134
Adjusted r^2..............: 0.015988
Sample size of AE DB......: 2423
Sample size of model......: 146
Missing data %............: 93.97441
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age
-0.77135 -0.14147 0.01298
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.11663 -0.57160 -0.04116 0.60758 2.03249
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.431478 144.523391 0.024 0.9811
currentDF[, TRAIT] -0.134759 0.076446 -1.763 0.0801 .
Age 0.011648 0.009083 1.282 0.2018
Gendermale 0.114498 0.171156 0.669 0.5046
ORdate_year -0.002093 0.072145 -0.029 0.9769
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9035 on 138 degrees of freedom
Multiple R-squared: 0.04307, Adjusted R-squared: 0.01533
F-statistic: 1.553 on 4 and 138 DF, p-value: 0.1904
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MMP2_rank
Effect size...............: -0.134759
Standard error............: 0.076446
Odds ratio (effect size)..: 0.874
Lower 95% CI..............: 0.752
Upper 95% CI..............: 1.015
T-value...................: -1.762815
P-value...................: 0.08014529
R^2.......................: 0.043068
Adjusted r^2..............: 0.015331
Sample size of AE DB......: 2423
Sample size of model......: 143
Missing data %............: 94.09822
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.79654 0.01314
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.97582 -0.58979 -0.03655 0.56989 2.07988
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -31.871929 144.530754 -0.221 0.826
currentDF[, TRAIT] 0.075997 0.076600 0.992 0.323
Age 0.010959 0.009151 1.198 0.233
Gendermale 0.131125 0.172836 0.759 0.449
ORdate_year 0.015540 0.072150 0.215 0.830
Residual standard error: 0.9103 on 138 degrees of freedom
Multiple R-squared: 0.02845, Adjusted R-squared: 0.0002887
F-statistic: 1.01 on 4 and 138 DF, p-value: 0.4044
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.075997
Standard error............: 0.0766
Odds ratio (effect size)..: 1.079
Lower 95% CI..............: 0.929
Upper 95% CI..............: 1.254
T-value...................: 0.992121
P-value...................: 0.3228749
R^2.......................: 0.02845
Adjusted r^2..............: 0.000289
Sample size of AE DB......: 2423
Sample size of model......: 143
Missing data %............: 94.09822
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age, data = currentDF)
Coefficients:
(Intercept) Age
-0.79654 0.01314
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.9490 -0.6007 -0.0525 0.5394 2.0989
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -23.936870 147.558703 -0.162 0.871
currentDF[, TRAIT] -0.011912 0.074294 -0.160 0.873
Age 0.011324 0.009222 1.228 0.222
Gendermale 0.159957 0.171423 0.933 0.352
ORdate_year 0.011555 0.073666 0.157 0.876
Residual standard error: 0.9135 on 138 degrees of freedom
Multiple R-squared: 0.0217, Adjusted R-squared: -0.006654
F-statistic: 0.7653 on 4 and 138 DF, p-value: 0.5495
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MMP9_rank
Effect size...............: -0.011912
Standard error............: 0.074294
Odds ratio (effect size)..: 0.988
Lower 95% CI..............: 0.854
Upper 95% CI..............: 1.143
T-value...................: -0.16033
P-value...................: 0.8728556
R^2.......................: 0.021702
Adjusted r^2..............: -0.006654
Sample size of AE DB......: 2423
Sample size of model......: 143
Missing data %............: 94.09822 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")Writing results to Excel-file...### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")Removing intermediate files...rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
Model 2
In this model we correct for Age, Gender, year of surgery, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of plaque cytokines as a function of plasma/plaque MCP1 levels.
GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")Running linear regression...for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
PROTEIN = TRAITS.PROTEIN.RANK[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
for (trait in 1:length(proteins_of_interest_rank)) {
TRAIT = proteins_of_interest_rank[trait]
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
### univariate
fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year +
Hypertension.composite + DiabetesStatus + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
MedHx_CVD + stenose,
data = currentDF)
model_step <- stepAIC(fit, direction = "both", trace = FALSE)
print(model_step)
print(summary(fit))
GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
GLM.results = rbind(GLM.results, GLM.results.TEMP)
}
}
Analysis of MCP1_pg_ug_2015_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + SmokerStatus +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes
615.5007 -0.3072 -0.1525 0.3381 -0.2260
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7956 -0.6788 -0.1225 0.4649 3.0289
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 613.642400 125.508728 4.889 1.67e-06 ***
currentDF[, TRAIT] 0.027676 0.059364 0.466 0.6414
Age 0.002251 0.007366 0.306 0.7602
Gendermale 0.145360 0.127558 1.140 0.2554
ORdate_year -0.306641 0.062622 -4.897 1.61e-06 ***
Hypertension.compositeyes -0.203449 0.175004 -1.163 0.2460
DiabetesStatusDiabetes 0.070787 0.146730 0.482 0.6299
SmokerStatusEx-smoker -0.151713 0.130165 -1.166 0.2447
SmokerStatusNever smoked 0.434656 0.205554 2.115 0.0353 *
Med.Statin.LLDyes -0.223402 0.130177 -1.716 0.0872 .
Med.all.antiplateletyes -0.234830 0.215199 -1.091 0.2761
GFR_MDRD 0.001889 0.003323 0.568 0.5702
BMI -0.009784 0.017485 -0.560 0.5762
MedHx_CVDyes 0.132967 0.121373 1.096 0.2742
stenose50-70% 0.348964 1.055309 0.331 0.7411
stenose70-90% 1.008743 1.007385 1.001 0.3175
stenose90-99% 0.917999 1.005677 0.913 0.3621
stenose100% (Occlusion) -0.103507 1.187441 -0.087 0.9306
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9899 on 295 degrees of freedom
Multiple R-squared: 0.1454, Adjusted R-squared: 0.09617
F-statistic: 2.953 on 17 and 295 DF, p-value: 0.0001019
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL2_rank
Effect size...............: 0.027676
Standard error............: 0.059364
Odds ratio (effect size)..: 1.028
Lower 95% CI..............: 0.915
Upper 95% CI..............: 1.155
T-value...................: 0.466211
P-value...................: 0.6414086
R^2.......................: 0.145415
Adjusted r^2..............: 0.096167
Sample size of AE DB......: 2423
Sample size of model......: 313
Missing data %............: 87.08213
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + SmokerStatus +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes
621.7351 -0.3103 -0.1500 0.3840 -0.2509
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9222 -0.6884 -0.1104 0.5382 2.9604
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 610.398429 135.832425 4.494 1.04e-05 ***
currentDF[, TRAIT] -0.019979 0.062015 -0.322 0.7476
Age 0.002203 0.007607 0.290 0.7723
Gendermale 0.130571 0.133660 0.977 0.3295
ORdate_year -0.304827 0.067808 -4.495 1.03e-05 ***
Hypertension.compositeyes -0.231430 0.183018 -1.265 0.2071
DiabetesStatusDiabetes 0.055532 0.150352 0.369 0.7122
SmokerStatusEx-smoker -0.146383 0.132127 -1.108 0.2689
SmokerStatusNever smoked 0.501419 0.216572 2.315 0.0213 *
Med.Statin.LLDyes -0.269873 0.135578 -1.991 0.0475 *
Med.all.antiplateletyes -0.198656 0.225865 -0.880 0.3799
GFR_MDRD 0.002273 0.003613 0.629 0.5298
BMI -0.012140 0.017918 -0.678 0.4986
MedHx_CVDyes 0.161542 0.125540 1.287 0.1993
stenose70-90% 0.649646 0.337777 1.923 0.0555 .
stenose90-99% 0.620298 0.331331 1.872 0.0623 .
stenose100% (Occlusion) -0.449094 0.694095 -0.647 0.5182
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9805 on 271 degrees of freedom
Multiple R-squared: 0.1492, Adjusted R-squared: 0.09897
F-statistic: 2.97 on 16 and 271 DF, p-value: 0.0001439
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.019979
Standard error............: 0.062015
Odds ratio (effect size)..: 0.98
Lower 95% CI..............: 0.868
Upper 95% CI..............: 1.107
T-value...................: -0.322162
P-value...................: 0.7475783
R^2.......................: 0.149199
Adjusted r^2..............: 0.098967
Sample size of AE DB......: 2423
Sample size of model......: 288
Missing data %............: 88.11391
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + Hypertension.composite +
SmokerStatus + Med.Statin.LLD + MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) ORdate_year Hypertension.compositeyes SmokerStatusEx-smoker SmokerStatusNever smoked
628.1153 -0.3134 -0.2538 -0.1036 0.4129
Med.Statin.LLDyes MedHx_CVDyes
-0.2648 0.1727
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9484 -0.6586 -0.1040 0.5198 2.9219
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 648.323041 125.147778 5.180 4.15e-07 ***
currentDF[, TRAIT] -0.011900 0.056752 -0.210 0.8341
Age 0.002391 0.007201 0.332 0.7401
Gendermale 0.147712 0.124021 1.191 0.2346
ORdate_year -0.323721 0.062477 -5.181 4.13e-07 ***
Hypertension.compositeyes -0.284110 0.168741 -1.684 0.0933 .
DiabetesStatusDiabetes -0.031655 0.144049 -0.220 0.8262
SmokerStatusEx-smoker -0.128999 0.125172 -1.031 0.3036
SmokerStatusNever smoked 0.444450 0.207565 2.141 0.0331 *
Med.Statin.LLDyes -0.264089 0.126263 -2.092 0.0373 *
Med.all.antiplateletyes -0.210315 0.214801 -0.979 0.3283
GFR_MDRD 0.001569 0.003293 0.476 0.6342
BMI -0.004812 0.016557 -0.291 0.7715
MedHx_CVDyes 0.168841 0.117383 1.438 0.1514
stenose70-90% 0.473300 0.322888 1.466 0.1438
stenose90-99% 0.440001 0.319818 1.376 0.1699
stenose100% (Occlusion) -0.616570 0.674146 -0.915 0.3612
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9548 on 290 degrees of freedom
Multiple R-squared: 0.1557, Adjusted R-squared: 0.1091
F-statistic: 3.342 on 16 and 290 DF, p-value: 2.097e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.0119
Standard error............: 0.056752
Odds ratio (effect size)..: 0.988
Lower 95% CI..............: 0.884
Upper 95% CI..............: 1.104
T-value...................: -0.209679
P-value...................: 0.8340655
R^2.......................: 0.155698
Adjusted r^2..............: 0.109115
Sample size of AE DB......: 2423
Sample size of model......: 307
Missing data %............: 87.32976
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + SmokerStatus +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes
732.4358 -0.3655 -0.1513 0.3019 -0.2413
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7273 -0.7096 -0.1205 0.4818 3.1135
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.505e+02 1.256e+02 5.973 6.61e-09 ***
currentDF[, TRAIT] 6.789e-02 5.743e-02 1.182 0.2381
Age 6.148e-03 7.691e-03 0.799 0.4247
Gendermale 1.534e-01 1.292e-01 1.187 0.2361
ORdate_year -3.747e-01 6.267e-02 -5.980 6.39e-09 ***
Hypertension.compositeyes -1.934e-01 1.789e-01 -1.081 0.2805
DiabetesStatusDiabetes 6.056e-02 1.496e-01 0.405 0.6859
SmokerStatusEx-smoker -1.824e-01 1.307e-01 -1.395 0.1639
SmokerStatusNever smoked 3.598e-01 2.104e-01 1.710 0.0883 .
Med.Statin.LLDyes -2.314e-01 1.311e-01 -1.765 0.0785 .
Med.all.antiplateletyes -1.279e-01 2.073e-01 -0.617 0.5376
GFR_MDRD 6.149e-04 3.459e-03 0.178 0.8590
BMI -5.853e-03 1.642e-02 -0.356 0.7218
MedHx_CVDyes 1.306e-01 1.217e-01 1.073 0.2843
stenose50-70% -3.683e-01 7.862e-01 -0.468 0.6398
stenose70-90% 2.723e-01 7.332e-01 0.371 0.7107
stenose90-99% 1.385e-01 7.297e-01 0.190 0.8496
stenose100% (Occlusion) -7.306e-01 9.729e-01 -0.751 0.4533
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.006 on 298 degrees of freedom
Multiple R-squared: 0.1694, Adjusted R-squared: 0.122
F-statistic: 3.576 on 17 and 298 DF, p-value: 3.668e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.06789
Standard error............: 0.057431
Odds ratio (effect size)..: 1.07
Lower 95% CI..............: 0.956
Upper 95% CI..............: 1.198
T-value...................: 1.182126
P-value...................: 0.2380982
R^2.......................: 0.169421
Adjusted r^2..............: 0.122039
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Med.Statin.LLDyes
875.8182 0.2330 -0.4372 -0.1959
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.07802 -0.64942 -0.08319 0.51249 2.91395
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 867.571807 120.477390 7.201 5.19e-12 ***
currentDF[, TRAIT] 0.219272 0.059164 3.706 0.000252 ***
Age -0.007571 0.007468 -1.014 0.311489
Gendermale 0.168019 0.128143 1.311 0.190835
ORdate_year -0.432529 0.060101 -7.197 5.34e-12 ***
Hypertension.compositeyes -0.183631 0.176490 -1.040 0.298994
DiabetesStatusDiabetes 0.131616 0.147382 0.893 0.372584
SmokerStatusEx-smoker -0.047867 0.127183 -0.376 0.706924
SmokerStatusNever smoked 0.373000 0.212502 1.755 0.080272 .
Med.Statin.LLDyes -0.237552 0.127035 -1.870 0.062498 .
Med.all.antiplateletyes 0.024058 0.204007 0.118 0.906208
GFR_MDRD -0.004054 0.003230 -1.255 0.210366
BMI -0.012442 0.015952 -0.780 0.436025
MedHx_CVDyes 0.113554 0.119771 0.948 0.343872
stenose50-70% -0.544387 0.769620 -0.707 0.479922
stenose70-90% 0.131755 0.710206 0.186 0.852954
stenose90-99% 0.008387 0.707602 0.012 0.990552
stenose100% (Occlusion) -0.148888 0.918575 -0.162 0.871352
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9771 on 289 degrees of freedom
Multiple R-squared: 0.2223, Adjusted R-squared: 0.1766
F-statistic: 4.86 on 17 and 289 DF, p-value: 3.565e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.219272
Standard error............: 0.059164
Odds ratio (effect size)..: 1.245
Lower 95% CI..............: 1.109
Upper 95% CI..............: 1.398
T-value...................: 3.706207
P-value...................: 0.0002521053
R^2.......................: 0.222327
Adjusted r^2..............: 0.176582
Sample size of AE DB......: 2423
Sample size of model......: 307
Missing data %............: 87.32976
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
DiabetesStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes
601.7963 0.1150 -0.3004 0.2550 -0.2609
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9338 -0.7449 -0.1154 0.5253 3.2595
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 616.528181 108.018182 5.708 2.56e-08 ***
currentDF[, TRAIT] 0.101264 0.058603 1.728 0.0849 .
Age -0.008338 0.007412 -1.125 0.2614
Gendermale 0.154155 0.127865 1.206 0.2288
ORdate_year -0.307003 0.053900 -5.696 2.73e-08 ***
Hypertension.compositeyes -0.219462 0.176590 -1.243 0.2148
DiabetesStatusDiabetes 0.279741 0.151375 1.848 0.0655 .
SmokerStatusEx-smoker -0.020944 0.130842 -0.160 0.8729
SmokerStatusNever smoked 0.299785 0.195130 1.536 0.1254
Med.Statin.LLDyes -0.276436 0.134660 -2.053 0.0409 *
Med.all.antiplateletyes -0.130929 0.214849 -0.609 0.5427
GFR_MDRD -0.003512 0.003213 -1.093 0.2752
BMI -0.021698 0.015775 -1.375 0.1699
MedHx_CVDyes 0.102909 0.121882 0.844 0.3991
stenose50-70% -0.371045 0.818048 -0.454 0.6504
stenose70-90% 0.112735 0.762885 0.148 0.8826
stenose90-99% 0.042890 0.759316 0.056 0.9550
stenose100% (Occlusion) -0.763627 0.943304 -0.810 0.4188
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.054 on 328 degrees of freedom
Multiple R-squared: 0.1565, Adjusted R-squared: 0.1128
F-statistic: 3.58 on 17 and 328 DF, p-value: 3.165e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.101264
Standard error............: 0.058603
Odds ratio (effect size)..: 1.107
Lower 95% CI..............: 0.986
Upper 95% CI..............: 1.241
T-value...................: 1.727962
P-value...................: 0.08493631
R^2.......................: 0.156502
Adjusted r^2..............: 0.112784
Sample size of AE DB......: 2423
Sample size of model......: 346
Missing data %............: 85.72018
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + SmokerStatus +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked
629.1322 0.2289 -0.3140 -0.2320 0.4039
Med.Statin.LLDyes
-0.2948
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8396 -0.6657 -0.1293 0.5137 3.0367
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 614.566254 145.630431 4.220 3.39e-05 ***
currentDF[, TRAIT] 0.008752 0.065775 0.133 0.8942
Age 0.001030 0.007899 0.130 0.8963
Gendermale 0.222461 0.137717 1.615 0.1075
ORdate_year -0.306768 0.072677 -4.221 3.37e-05 ***
Hypertension.compositeyes -0.246419 0.191160 -1.289 0.1985
DiabetesStatusDiabetes 0.042441 0.158048 0.269 0.7885
SmokerStatusEx-smoker -0.202212 0.136751 -1.479 0.1404
SmokerStatusNever smoked 0.479917 0.223206 2.150 0.0325 *
Med.Statin.LLDyes -0.291398 0.138329 -2.107 0.0361 *
Med.all.antiplateletyes -0.257652 0.235015 -1.096 0.2740
GFR_MDRD 0.001191 0.003724 0.320 0.7494
BMI -0.008942 0.018079 -0.495 0.6213
MedHx_CVDyes 0.102880 0.132575 0.776 0.4385
stenose70-90% 0.477578 0.359593 1.328 0.1853
stenose90-99% 0.484577 0.354099 1.368 0.1724
stenose100% (Occlusion) -0.633370 0.716073 -0.885 0.3772
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.993 on 258 degrees of freedom
Multiple R-squared: 0.1473, Adjusted R-squared: 0.09439
F-statistic: 2.785 on 16 and 258 DF, p-value: 0.0003687
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL10_rank
Effect size...............: 0.008752
Standard error............: 0.065775
Odds ratio (effect size)..: 1.009
Lower 95% CI..............: 0.887
Upper 95% CI..............: 1.148
T-value...................: 0.133062
P-value...................: 0.894248
R^2.......................: 0.147273
Adjusted r^2..............: 0.09439
Sample size of AE DB......: 2423
Sample size of model......: 275
Missing data %............: 88.65043
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Hypertension.composite +
SmokerStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year Hypertension.compositeyes SmokerStatusEx-smoker
707.6392 0.1905 -0.3531 -0.3049 -0.1432
SmokerStatusNever smoked Med.Statin.LLDyes
0.4631 -0.1950
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7950 -0.6672 -0.1153 0.5208 3.0267
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 6.733e+02 1.384e+02 4.865 1.95e-06 ***
currentDF[, TRAIT] 2.084e-02 6.262e-02 0.333 0.7395
Age 3.782e-03 7.751e-03 0.488 0.6260
Gendermale 1.841e-01 1.322e-01 1.392 0.1649
ORdate_year -3.362e-01 6.906e-02 -4.868 1.92e-06 ***
Hypertension.compositeyes -3.444e-01 1.851e-01 -1.860 0.0639 .
DiabetesStatusDiabetes 9.014e-02 1.520e-01 0.593 0.5537
SmokerStatusEx-smoker -1.568e-01 1.354e-01 -1.158 0.2481
SmokerStatusNever smoked 4.851e-01 2.102e-01 2.308 0.0218 *
Med.Statin.LLDyes -2.089e-01 1.350e-01 -1.547 0.1230
Med.all.antiplateletyes -1.619e-01 2.261e-01 -0.716 0.4747
GFR_MDRD 5.868e-04 3.590e-03 0.163 0.8703
BMI -1.158e-02 1.792e-02 -0.646 0.5187
MedHx_CVDyes 1.410e-01 1.268e-01 1.112 0.2670
stenose70-90% 6.102e-01 3.365e-01 1.813 0.0709 .
stenose90-99% 5.596e-01 3.314e-01 1.689 0.0925 .
stenose100% (Occlusion) -3.804e-03 8.081e-01 -0.005 0.9962
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9778 on 270 degrees of freedom
Multiple R-squared: 0.1548, Adjusted R-squared: 0.1047
F-statistic: 3.09 on 16 and 270 DF, p-value: 8.009e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL12_rank
Effect size...............: 0.020844
Standard error............: 0.062623
Odds ratio (effect size)..: 1.021
Lower 95% CI..............: 0.903
Upper 95% CI..............: 1.154
T-value...................: 0.332848
P-value...................: 0.7395077
R^2.......................: 0.15475
Adjusted r^2..............: 0.104661
Sample size of AE DB......: 2423
Sample size of model......: 287
Missing data %............: 88.15518
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Med.Statin.LLDyes
621.1030 0.1496 -0.3100 -0.2002
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9634 -0.7043 -0.1166 0.4844 3.2372
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 640.523985 101.352594 6.320 7.44e-10 ***
currentDF[, TRAIT] 0.130932 0.054106 2.420 0.0160 *
Age -0.006342 0.006698 -0.947 0.3443
Gendermale 0.127528 0.115513 1.104 0.2703
ORdate_year -0.319117 0.050570 -6.310 7.86e-10 ***
Hypertension.compositeyes -0.098374 0.157054 -0.626 0.5315
DiabetesStatusDiabetes 0.153219 0.132163 1.159 0.2471
SmokerStatusEx-smoker -0.032123 0.117461 -0.273 0.7846
SmokerStatusNever smoked 0.287178 0.181150 1.585 0.1137
Med.Statin.LLDyes -0.228262 0.121712 -1.875 0.0615 .
Med.all.antiplateletyes -0.162972 0.185153 -0.880 0.3793
GFR_MDRD -0.002524 0.002967 -0.850 0.3956
BMI -0.020034 0.014319 -1.399 0.1626
MedHx_CVDyes 0.088478 0.109524 0.808 0.4197
stenose50-70% -0.446479 0.784110 -0.569 0.5694
stenose70-90% 0.110453 0.737703 0.150 0.8811
stenose90-99% -0.004374 0.735639 -0.006 0.9953
stenose100% (Occlusion) -0.761030 0.908852 -0.837 0.4029
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.02 on 375 degrees of freedom
Multiple R-squared: 0.1526, Adjusted R-squared: 0.1142
F-statistic: 3.972 on 17 and 375 DF, p-value: 2.961e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.130932
Standard error............: 0.054106
Odds ratio (effect size)..: 1.14
Lower 95% CI..............: 1.025
Upper 95% CI..............: 1.267
T-value...................: 2.41994
P-value...................: 0.01599776
R^2.......................: 0.152587
Adjusted r^2..............: 0.114171
Sample size of AE DB......: 2423
Sample size of model......: 393
Missing data %............: 83.78044
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Med.Statin.LLDyes
611.6746 0.1404 -0.3053 -0.1976
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9492 -0.7054 -0.1019 0.4619 3.1900
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 631.553561 101.101401 6.247 1.14e-09 ***
currentDF[, TRAIT] 0.120187 0.054719 2.196 0.0287 *
Age -0.006807 0.006666 -1.021 0.3078
Gendermale 0.123682 0.115386 1.072 0.2845
ORdate_year -0.314613 0.050444 -6.237 1.20e-09 ***
Hypertension.compositeyes -0.107626 0.156883 -0.686 0.4931
DiabetesStatusDiabetes 0.143268 0.131965 1.086 0.2783
SmokerStatusEx-smoker -0.026662 0.117214 -0.227 0.8202
SmokerStatusNever smoked 0.292399 0.181111 1.614 0.1073
Med.Statin.LLDyes -0.229425 0.121445 -1.889 0.0596 .
Med.all.antiplateletyes -0.158811 0.185110 -0.858 0.3915
GFR_MDRD -0.002574 0.002967 -0.868 0.3862
BMI -0.020583 0.014311 -1.438 0.1512
MedHx_CVDyes 0.091917 0.109197 0.842 0.4005
stenose50-70% -0.456601 0.784870 -0.582 0.5611
stenose70-90% 0.110159 0.738255 0.149 0.8815
stenose90-99% -0.004107 0.736301 -0.006 0.9956
stenose100% (Occlusion) -0.796025 0.909271 -0.875 0.3819
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.02 on 376 degrees of freedom
Multiple R-squared: 0.1502, Adjusted R-squared: 0.1118
F-statistic: 3.91 on 17 and 376 DF, p-value: 4.186e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.120187
Standard error............: 0.054719
Odds ratio (effect size)..: 1.128
Lower 95% CI..............: 1.013
Upper 95% CI..............: 1.255
T-value...................: 2.196435
P-value...................: 0.02867072
R^2.......................: 0.150229
Adjusted r^2..............: 0.111809
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Hypertension.composite +
SmokerStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year Hypertension.compositeyes SmokerStatusEx-smoker
784.5257 0.2412 -0.3915 -0.2532 -0.1254
SmokerStatusNever smoked Med.Statin.LLDyes
0.3806 -0.2133
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8110 -0.6698 -0.0809 0.5140 3.0236
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.784e+02 1.326e+02 5.872 1.19e-08 ***
currentDF[, TRAIT] 1.164e-02 6.807e-02 0.171 0.8643
Age -2.916e-03 7.526e-03 -0.388 0.6987
Gendermale 2.453e-01 1.312e-01 1.869 0.0626 .
ORdate_year -3.882e-01 6.614e-02 -5.869 1.20e-08 ***
Hypertension.compositeyes -2.811e-01 1.813e-01 -1.551 0.1221
DiabetesStatusDiabetes 7.881e-02 1.479e-01 0.533 0.5946
SmokerStatusEx-smoker -1.195e-01 1.304e-01 -0.916 0.3604
SmokerStatusNever smoked 4.351e-01 2.117e-01 2.055 0.0407 *
Med.Statin.LLDyes -2.372e-01 1.352e-01 -1.754 0.0805 .
Med.all.antiplateletyes -2.225e-01 2.067e-01 -1.076 0.2827
GFR_MDRD -6.679e-04 3.374e-03 -0.198 0.8432
BMI -1.052e-02 1.611e-02 -0.653 0.5143
MedHx_CVDyes 1.139e-01 1.255e-01 0.908 0.3648
stenose50-70% -4.742e-01 7.964e-01 -0.595 0.5520
stenose70-90% 1.116e-01 7.317e-01 0.153 0.8789
stenose90-99% 2.541e-02 7.272e-01 0.035 0.9722
stenose100% (Occlusion) -5.239e-01 1.045e+00 -0.501 0.6167
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9984 on 288 degrees of freedom
Multiple R-squared: 0.1744, Adjusted R-squared: 0.1257
F-statistic: 3.579 on 17 and 288 DF, p-value: 3.782e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: INFG_rank
Effect size...............: 0.01164
Standard error............: 0.06807
Odds ratio (effect size)..: 1.012
Lower 95% CI..............: 0.885
Upper 95% CI..............: 1.156
T-value...................: 0.170995
P-value...................: 0.8643475
R^2.......................: 0.174399
Adjusted r^2..............: 0.125665
Sample size of AE DB......: 2423
Sample size of model......: 306
Missing data %............: 87.37103
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) ORdate_year Med.Statin.LLDyes
752.1760 -0.3754 -0.2382
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.85176 -0.70666 -0.06778 0.52172 2.96247
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.291e+02 1.368e+02 5.329 2.13e-07 ***
currentDF[, TRAIT] 4.018e-02 6.089e-02 0.660 0.5099
Age 1.899e-03 7.598e-03 0.250 0.8028
Gendermale 1.735e-01 1.318e-01 1.316 0.1892
ORdate_year -3.641e-01 6.830e-02 -5.331 2.11e-07 ***
Hypertension.compositeyes -2.628e-01 1.820e-01 -1.444 0.1499
DiabetesStatusDiabetes -8.969e-02 1.507e-01 -0.595 0.5523
SmokerStatusEx-smoker -9.624e-02 1.336e-01 -0.720 0.4720
SmokerStatusNever smoked 3.855e-01 2.136e-01 1.805 0.0723 .
Med.Statin.LLDyes -2.171e-01 1.330e-01 -1.632 0.1039
Med.all.antiplateletyes -1.231e-01 2.278e-01 -0.540 0.5895
GFR_MDRD 7.723e-04 3.523e-03 0.219 0.8266
BMI -8.022e-03 1.785e-02 -0.450 0.6534
MedHx_CVDyes 1.265e-01 1.271e-01 0.995 0.3207
stenose70-90% 5.637e-01 3.331e-01 1.692 0.0918 .
stenose90-99% 5.357e-01 3.270e-01 1.638 0.1026
stenose100% (Occlusion) 4.313e-02 8.000e-01 0.054 0.9570
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9617 on 262 degrees of freedom
Multiple R-squared: 0.1607, Adjusted R-squared: 0.1094
F-statistic: 3.135 on 16 and 262 DF, p-value: 6.575e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TNFA_rank
Effect size...............: 0.040178
Standard error............: 0.060889
Odds ratio (effect size)..: 1.041
Lower 95% CI..............: 0.924
Upper 95% CI..............: 1.173
T-value...................: 0.65985
P-value...................: 0.5099298
R^2.......................: 0.160691
Adjusted r^2..............: 0.109436
Sample size of AE DB......: 2423
Sample size of model......: 279
Missing data %............: 88.48535
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Med.Statin.LLDyes
530.48694 0.09001 -0.26479 -0.19935
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8502 -0.7147 -0.1246 0.5322 2.9886
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 561.886413 109.627605 5.125 4.76e-07 ***
currentDF[, TRAIT] 0.083987 0.060146 1.396 0.1634
Age -0.007989 0.006654 -1.201 0.2306
Gendermale 0.136229 0.115633 1.178 0.2395
ORdate_year -0.279848 0.054700 -5.116 4.99e-07 ***
Hypertension.compositeyes -0.104558 0.157707 -0.663 0.5077
DiabetesStatusDiabetes 0.156115 0.133426 1.170 0.2427
SmokerStatusEx-smoker 0.006856 0.117974 0.058 0.9537
SmokerStatusNever smoked 0.350452 0.180755 1.939 0.0533 .
Med.Statin.LLDyes -0.233610 0.121923 -1.916 0.0561 .
Med.all.antiplateletyes -0.165048 0.186344 -0.886 0.3763
GFR_MDRD -0.002368 0.002998 -0.790 0.4301
BMI -0.022501 0.014385 -1.564 0.1186
MedHx_CVDyes 0.087763 0.109802 0.799 0.4246
stenose50-70% -0.359343 0.785883 -0.457 0.6478
stenose70-90% 0.201097 0.739110 0.272 0.7857
stenose90-99% 0.088262 0.737060 0.120 0.9047
stenose100% (Occlusion) -0.754526 0.912390 -0.827 0.4088
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.024 on 376 degrees of freedom
Multiple R-squared: 0.1438, Adjusted R-squared: 0.1051
F-statistic: 3.714 on 17 and 376 DF, p-value: 1.269e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.083987
Standard error............: 0.060146
Odds ratio (effect size)..: 1.088
Lower 95% CI..............: 0.967
Upper 95% CI..............: 1.224
T-value...................: 1.396393
P-value...................: 0.1634199
R^2.......................: 0.143767
Adjusted r^2..............: 0.105054
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
DiabetesStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes
508.4257 0.2312 -0.2538 0.1780 -0.1810
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.13959 -0.69973 -0.07668 0.51874 2.86017
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 525.306082 102.093906 5.145 4.33e-07 ***
currentDF[, TRAIT] 0.206906 0.052371 3.951 9.32e-05 ***
Age -0.007527 0.006604 -1.140 0.2551
Gendermale 0.106705 0.114982 0.928 0.3540
ORdate_year -0.261754 0.050933 -5.139 4.46e-07 ***
Hypertension.compositeyes -0.082266 0.155291 -0.530 0.5966
DiabetesStatusDiabetes 0.189982 0.131687 1.443 0.1500
SmokerStatusEx-smoker -0.003971 0.116002 -0.034 0.9727
SmokerStatusNever smoked 0.300941 0.178022 1.690 0.0918 .
Med.Statin.LLDyes -0.205724 0.120436 -1.708 0.0884 .
Med.all.antiplateletyes -0.118437 0.187733 -0.631 0.5285
GFR_MDRD -0.002948 0.002935 -1.004 0.3159
BMI -0.016728 0.014231 -1.175 0.2406
MedHx_CVDyes 0.073295 0.108566 0.675 0.5000
stenose50-70% -0.235398 0.771672 -0.305 0.7605
stenose70-90% 0.306364 0.725080 0.423 0.6729
stenose90-99% 0.232534 0.722573 0.322 0.7478
stenose100% (Occlusion) -0.395377 0.899637 -0.439 0.6606
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.006 on 372 degrees of freedom
Multiple R-squared: 0.1731, Adjusted R-squared: 0.1353
F-statistic: 4.58 on 17 and 372 DF, p-value: 9.395e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 0.206906
Standard error............: 0.052371
Odds ratio (effect size)..: 1.23
Lower 95% CI..............: 1.11
Upper 95% CI..............: 1.363
T-value...................: 3.950765
P-value...................: 9.32095e-05
R^2.......................: 0.173067
Adjusted r^2..............: 0.135277
Sample size of AE DB......: 2423
Sample size of model......: 390
Missing data %............: 83.90425
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
DiabetesStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes
598.5494 0.1518 -0.2988 0.2238 -0.2769
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9331 -0.7349 -0.1001 0.5107 3.2356
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 608.930050 105.698714 5.761 1.88e-08 ***
currentDF[, TRAIT] 0.139384 0.058297 2.391 0.0174 *
Age -0.008152 0.007222 -1.129 0.2598
Gendermale 0.131240 0.125658 1.044 0.2970
ORdate_year -0.303204 0.052740 -5.749 2.00e-08 ***
Hypertension.compositeyes -0.204706 0.169957 -1.204 0.2293
DiabetesStatusDiabetes 0.240926 0.143645 1.677 0.0944 .
SmokerStatusEx-smoker -0.038570 0.127276 -0.303 0.7620
SmokerStatusNever smoked 0.264892 0.191128 1.386 0.1667
Med.Statin.LLDyes -0.291986 0.131317 -2.224 0.0268 *
Med.all.antiplateletyes -0.131454 0.208657 -0.630 0.5291
GFR_MDRD -0.002852 0.003114 -0.916 0.3603
BMI -0.019721 0.015187 -1.299 0.1950
MedHx_CVDyes 0.091248 0.118869 0.768 0.4432
stenose50-70% -0.463998 0.811932 -0.571 0.5681
stenose70-90% 0.020301 0.757482 0.027 0.9786
stenose90-99% -0.064065 0.754593 -0.085 0.9324
stenose100% (Occlusion) -0.841410 0.934864 -0.900 0.3687
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.044 on 339 degrees of freedom
Multiple R-squared: 0.158, Adjusted R-squared: 0.1158
F-statistic: 3.742 on 17 and 339 DF, p-value: 1.239e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.139384
Standard error............: 0.058297
Odds ratio (effect size)..: 1.15
Lower 95% CI..............: 1.025
Upper 95% CI..............: 1.289
T-value...................: 2.390946
P-value...................: 0.01735002
R^2.......................: 0.158
Adjusted r^2..............: 0.115776
Sample size of AE DB......: 2423
Sample size of model......: 357
Missing data %............: 85.2662
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) ORdate_year Med.Statin.LLDyes
592.5051 -0.2957 -0.2136
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8771 -0.7136 -0.1137 0.5285 3.0249
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 577.595126 108.817363 5.308 1.92e-07 ***
currentDF[, TRAIT] 0.048611 0.057177 0.850 0.3958
Age -0.008761 0.006743 -1.299 0.1947
Gendermale 0.138695 0.118235 1.173 0.2415
ORdate_year -0.287677 0.054292 -5.299 2.01e-07 ***
Hypertension.compositeyes -0.147463 0.160766 -0.917 0.3596
DiabetesStatusDiabetes 0.170295 0.135936 1.253 0.2111
SmokerStatusEx-smoker 0.013369 0.119113 0.112 0.9107
SmokerStatusNever smoked 0.350934 0.182239 1.926 0.0549 .
Med.Statin.LLDyes -0.246803 0.123522 -1.998 0.0464 *
Med.all.antiplateletyes -0.109622 0.192672 -0.569 0.5697
GFR_MDRD -0.003294 0.003010 -1.094 0.2746
BMI -0.021287 0.014571 -1.461 0.1449
MedHx_CVDyes 0.100032 0.112001 0.893 0.3724
stenose50-70% -0.347454 0.793462 -0.438 0.6617
stenose70-90% 0.227627 0.742174 0.307 0.7592
stenose90-99% 0.149435 0.739217 0.202 0.8399
stenose100% (Occlusion) -0.744097 0.919429 -0.809 0.4189
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.029 on 369 degrees of freedom
Multiple R-squared: 0.1407, Adjusted R-squared: 0.1011
F-statistic: 3.555 on 17 and 369 DF, p-value: 3.154e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.048611
Standard error............: 0.057177
Odds ratio (effect size)..: 1.05
Lower 95% CI..............: 0.939
Upper 95% CI..............: 1.174
T-value...................: 0.850177
P-value...................: 0.3957782
R^2.......................: 0.140732
Adjusted r^2..............: 0.101145
Sample size of AE DB......: 2423
Sample size of model......: 387
Missing data %............: 84.02806
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Med.Statin.LLDyes
645.2054 0.1032 -0.3220 -0.2155
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9559 -0.7249 -0.1105 0.5005 3.1769
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 655.828872 107.001699 6.129 2.29e-09 ***
currentDF[, TRAIT] 0.088823 0.057999 1.531 0.1265
Age -0.008243 0.006896 -1.195 0.2327
Gendermale 0.114799 0.118292 0.970 0.3325
ORdate_year -0.326650 0.053391 -6.118 2.44e-09 ***
Hypertension.compositeyes -0.128855 0.161159 -0.800 0.4245
DiabetesStatusDiabetes 0.179953 0.136289 1.320 0.1875
SmokerStatusEx-smoker -0.020769 0.120467 -0.172 0.8632
SmokerStatusNever smoked 0.250697 0.187064 1.340 0.1810
Med.Statin.LLDyes -0.252564 0.124323 -2.032 0.0429 *
Med.all.antiplateletyes -0.179761 0.190323 -0.945 0.3455
GFR_MDRD -0.003050 0.003018 -1.011 0.3129
BMI -0.019849 0.014575 -1.362 0.1741
MedHx_CVDyes 0.066653 0.112785 0.591 0.5549
stenose50-70% -0.450359 0.797819 -0.564 0.5728
stenose70-90% 0.132329 0.746553 0.177 0.8594
stenose90-99% 0.036946 0.744165 0.050 0.9604
stenose100% (Occlusion) -0.827718 0.921141 -0.899 0.3695
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.031 on 365 degrees of freedom
Multiple R-squared: 0.1439, Adjusted R-squared: 0.1041
F-statistic: 3.61 on 17 and 365 DF, p-value: 2.351e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.088823
Standard error............: 0.057999
Odds ratio (effect size)..: 1.093
Lower 95% CI..............: 0.975
Upper 95% CI..............: 1.224
T-value...................: 1.531475
P-value...................: 0.1265182
R^2.......................: 0.143935
Adjusted r^2..............: 0.104064
Sample size of AE DB......: 2423
Sample size of model......: 383
Missing data %............: 84.19315
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
DiabetesStatus + Med.Statin.LLD + MedHx_CVD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes MedHx_CVDyes
603.7713 0.1550 -0.3014 0.2302 -0.2633 0.1700
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.89044 -0.73576 -0.05783 0.56768 2.92379
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 608.443344 104.787343 5.806 1.49e-08 ***
currentDF[, TRAIT] 0.146556 0.056745 2.583 0.0102 *
Age -0.005154 0.007198 -0.716 0.4744
Gendermale 0.133516 0.122306 1.092 0.2758
ORdate_year -0.303176 0.052282 -5.799 1.56e-08 ***
Hypertension.compositeyes -0.186912 0.166735 -1.121 0.2631
DiabetesStatusDiabetes 0.235403 0.142647 1.650 0.0998 .
SmokerStatusEx-smoker -0.051917 0.126078 -0.412 0.6808
SmokerStatusNever smoked 0.116580 0.196243 0.594 0.5529
Med.Statin.LLDyes -0.279878 0.128615 -2.176 0.0303 *
Med.all.antiplateletyes -0.167419 0.194462 -0.861 0.3899
GFR_MDRD -0.003234 0.003125 -1.035 0.3015
BMI -0.015385 0.015108 -1.018 0.3093
MedHx_CVDyes 0.182782 0.116555 1.568 0.1178
stenose50-70% -0.403262 0.789012 -0.511 0.6096
stenose70-90% 0.141061 0.739244 0.191 0.8488
stenose90-99% 0.032843 0.735707 0.045 0.9644
stenose100% (Occlusion) -0.629685 0.913084 -0.690 0.4909
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.021 on 332 degrees of freedom
Multiple R-squared: 0.1678, Adjusted R-squared: 0.1252
F-statistic: 3.938 on 17 and 332 DF, p-value: 4.307e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.146556
Standard error............: 0.056745
Odds ratio (effect size)..: 1.158
Lower 95% CI..............: 1.036
Upper 95% CI..............: 1.294
T-value...................: 2.582702
P-value...................: 0.01023056
R^2.......................: 0.167803
Adjusted r^2..............: 0.125191
Sample size of AE DB......: 2423
Sample size of model......: 350
Missing data %............: 85.5551
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Med.Statin.LLDyes
621.96072 0.09579 -0.31044 -0.19274
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9758 -0.7011 -0.1045 0.5013 3.0768
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 636.648833 102.223868 6.228 1.27e-09 ***
currentDF[, TRAIT] 0.071373 0.056329 1.267 0.2059
Age -0.007885 0.006672 -1.182 0.2381
Gendermale 0.129283 0.116464 1.110 0.2677
ORdate_year -0.317144 0.051003 -6.218 1.34e-09 ***
Hypertension.compositeyes -0.116126 0.157485 -0.737 0.4614
DiabetesStatusDiabetes 0.137453 0.132491 1.037 0.3002
SmokerStatusEx-smoker -0.019695 0.117728 -0.167 0.8672
SmokerStatusNever smoked 0.309398 0.182121 1.699 0.0902 .
Med.Statin.LLDyes -0.227095 0.121992 -1.862 0.0634 .
Med.all.antiplateletyes -0.153511 0.185943 -0.826 0.4096
GFR_MDRD -0.002647 0.002982 -0.888 0.3754
BMI -0.020923 0.014371 -1.456 0.1463
MedHx_CVDyes 0.092707 0.109696 0.845 0.3986
stenose50-70% -0.406097 0.788644 -0.515 0.6069
stenose70-90% 0.163802 0.741880 0.221 0.8254
stenose90-99% 0.056827 0.739871 0.077 0.9388
stenose100% (Occlusion) -0.788912 0.913865 -0.863 0.3885
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.025 on 376 degrees of freedom
Multiple R-squared: 0.143, Adjusted R-squared: 0.1042
F-statistic: 3.69 on 17 and 376 DF, p-value: 1.448e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.071373
Standard error............: 0.056329
Odds ratio (effect size)..: 1.074
Lower 95% CI..............: 0.962
Upper 95% CI..............: 1.199
T-value...................: 1.267069
P-value...................: 0.2059147
R^2.......................: 0.142986
Adjusted r^2..............: 0.104238
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Med.Statin.LLDyes
540.0839 0.1410 -0.2696 -0.2553
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.9079 -0.6856 -0.1040 0.4814 3.1625
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 551.833212 125.564673 4.395 1.51e-05 ***
currentDF[, TRAIT] 0.118805 0.059963 1.981 0.0484 *
Age -0.009621 0.007130 -1.349 0.1782
Gendermale 0.122265 0.124542 0.982 0.3270
ORdate_year -0.274607 0.062670 -4.382 1.60e-05 ***
Hypertension.compositeyes -0.197456 0.167715 -1.177 0.2399
DiabetesStatusDiabetes 0.167506 0.144279 1.161 0.2465
SmokerStatusEx-smoker 0.001038 0.127759 0.008 0.9935
SmokerStatusNever smoked 0.296366 0.189226 1.566 0.1183
Med.Statin.LLDyes -0.294360 0.134703 -2.185 0.0296 *
Med.all.antiplateletyes -0.082747 0.201580 -0.410 0.6817
GFR_MDRD -0.005069 0.003285 -1.543 0.1238
BMI -0.023691 0.015766 -1.503 0.1339
MedHx_CVDyes 0.051347 0.120043 0.428 0.6691
stenose50-70% -0.328570 0.792777 -0.414 0.6788
stenose70-90% 0.166590 0.739111 0.225 0.8218
stenose90-99% 0.061904 0.736670 0.084 0.9331
stenose100% (Occlusion) -0.728236 0.916054 -0.795 0.4272
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.022 on 318 degrees of freedom
Multiple R-squared: 0.1433, Adjusted R-squared: 0.09752
F-statistic: 3.129 on 17 and 318 DF, p-value: 3.754e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.118805
Standard error............: 0.059963
Odds ratio (effect size)..: 1.126
Lower 95% CI..............: 1.001
Upper 95% CI..............: 1.267
T-value...................: 1.981291
P-value...................: 0.04841972
R^2.......................: 0.143315
Adjusted r^2..............: 0.097517
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Med.Statin.LLDyes
528.6136 0.1183 -0.2639 -0.1968
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8780 -0.7038 -0.1073 0.5359 2.9543
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 565.882347 106.579956 5.309 1.88e-07 ***
currentDF[, TRAIT] 0.092755 0.056767 1.634 0.1031
Age -0.008312 0.006627 -1.254 0.2105
Gendermale 0.148858 0.115050 1.294 0.1965
ORdate_year -0.281912 0.053165 -5.303 1.95e-07 ***
Hypertension.compositeyes -0.092312 0.158050 -0.584 0.5595
DiabetesStatusDiabetes 0.154136 0.132895 1.160 0.2469
SmokerStatusEx-smoker -0.006186 0.117301 -0.053 0.9580
SmokerStatusNever smoked 0.327456 0.180521 1.814 0.0705 .
Med.Statin.LLDyes -0.232841 0.121796 -1.912 0.0567 .
Med.all.antiplateletyes -0.122900 0.185900 -0.661 0.5089
GFR_MDRD -0.002580 0.002978 -0.866 0.3869
BMI -0.021324 0.014348 -1.486 0.1380
MedHx_CVDyes 0.090739 0.109555 0.828 0.4081
stenose50-70% -0.236917 0.786316 -0.301 0.7634
stenose70-90% 0.291530 0.737819 0.395 0.6930
stenose90-99% 0.185763 0.735119 0.253 0.8006
stenose100% (Occlusion) -0.577616 0.915986 -0.631 0.5287
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.023 on 376 degrees of freedom
Multiple R-squared: 0.1454, Adjusted R-squared: 0.1068
F-statistic: 3.763 on 17 and 376 DF, p-value: 9.616e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.092755
Standard error............: 0.056767
Odds ratio (effect size)..: 1.097
Lower 95% CI..............: 0.982
Upper 95% CI..............: 1.226
T-value...................: 1.633952
P-value...................: 0.1031058
R^2.......................: 0.145394
Adjusted r^2..............: 0.106755
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + DiabetesStatus +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes
607.8354 -0.3034 0.2240 -0.2736
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8964 -0.7101 -0.0881 0.5328 3.1153
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 588.018212 113.120695 5.198 3.50e-07 ***
currentDF[, TRAIT] 0.053196 0.061716 0.862 0.3893
Age -0.011011 0.007133 -1.544 0.1236
Gendermale 0.154530 0.125948 1.227 0.2207
ORdate_year -0.292681 0.056442 -5.186 3.73e-07 ***
Hypertension.compositeyes -0.208318 0.171844 -1.212 0.2263
DiabetesStatusDiabetes 0.262304 0.146626 1.789 0.0745 .
SmokerStatusEx-smoker 0.019710 0.127508 0.155 0.8772
SmokerStatusNever smoked 0.318275 0.192246 1.656 0.0987 .
Med.Statin.LLDyes -0.305245 0.131320 -2.324 0.0207 *
Med.all.antiplateletyes -0.127052 0.213930 -0.594 0.5530
GFR_MDRD -0.003608 0.003166 -1.140 0.2553
BMI -0.023458 0.015339 -1.529 0.1271
MedHx_CVDyes 0.109296 0.119684 0.913 0.3618
stenose50-70% -0.346333 0.813867 -0.426 0.6707
stenose70-90% 0.143983 0.759228 0.190 0.8497
stenose90-99% 0.055937 0.756441 0.074 0.9411
stenose100% (Occlusion) -0.790587 0.938322 -0.843 0.4001
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.049 on 336 degrees of freedom
Multiple R-squared: 0.1546, Adjusted R-squared: 0.1119
F-statistic: 3.615 on 17 and 336 DF, p-value: 2.528e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.053196
Standard error............: 0.061716
Odds ratio (effect size)..: 1.055
Lower 95% CI..............: 0.934
Upper 95% CI..............: 1.19
T-value...................: 0.861943
P-value...................: 0.3893336
R^2.......................: 0.154627
Adjusted r^2..............: 0.111855
Sample size of AE DB......: 2423
Sample size of model......: 354
Missing data %............: 85.39001
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Med.Statin.LLDyes
601.5367 0.1908 -0.3002 -0.1960
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.70686 -0.73910 -0.09815 0.50458 3.13359
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 620.549914 100.171190 6.195 1.54e-09 ***
currentDF[, TRAIT] 0.181479 0.051679 3.512 0.0005 ***
Age -0.006744 0.006591 -1.023 0.3069
Gendermale 0.109831 0.114746 0.957 0.3391
ORdate_year -0.309204 0.049980 -6.187 1.61e-09 ***
Hypertension.compositeyes -0.068235 0.156171 -0.437 0.6624
DiabetesStatusDiabetes 0.169241 0.131197 1.290 0.1979
SmokerStatusEx-smoker -0.039586 0.116413 -0.340 0.7340
SmokerStatusNever smoked 0.292052 0.178892 1.633 0.1034
Med.Statin.LLDyes -0.223452 0.120704 -1.851 0.0649 .
Med.all.antiplateletyes -0.188764 0.183880 -1.027 0.3053
GFR_MDRD -0.002906 0.002939 -0.989 0.3234
BMI -0.018824 0.014207 -1.325 0.1860
MedHx_CVDyes 0.090730 0.108568 0.836 0.4039
stenose50-70% -0.295606 0.775873 -0.381 0.7034
stenose70-90% 0.242496 0.729196 0.333 0.7397
stenose90-99% 0.135368 0.726612 0.186 0.8523
stenose100% (Occlusion) -0.523863 0.903035 -0.580 0.5622
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.012 on 375 degrees of freedom
Multiple R-squared: 0.1668, Adjusted R-squared: 0.129
F-statistic: 4.415 on 17 and 375 DF, p-value: 2.38e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.181479
Standard error............: 0.051679
Odds ratio (effect size)..: 1.199
Lower 95% CI..............: 1.083
Upper 95% CI..............: 1.327
T-value...................: 3.511632
P-value...................: 0.0004996369
R^2.......................: 0.166754
Adjusted r^2..............: 0.12898
Sample size of AE DB......: 2423
Sample size of model......: 393
Missing data %............: 83.78044
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Med.Statin.LLDyes
555.08643 0.09464 -0.27707 -0.18925
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.89939 -0.71156 -0.08992 0.53496 2.96745
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 587.901273 104.003175 5.653 3.13e-08 ***
currentDF[, TRAIT] 0.075162 0.054198 1.387 0.1663
Age -0.007377 0.006710 -1.099 0.2723
Gendermale 0.144824 0.115241 1.257 0.2096
ORdate_year -0.292867 0.051887 -5.644 3.27e-08 ***
Hypertension.compositeyes -0.114939 0.157428 -0.730 0.4658
DiabetesStatusDiabetes 0.148246 0.132851 1.116 0.2652
SmokerStatusEx-smoker -0.008019 0.117404 -0.068 0.9456
SmokerStatusNever smoked 0.322760 0.180934 1.784 0.0753 .
Med.Statin.LLDyes -0.223059 0.122031 -1.828 0.0684 .
Med.all.antiplateletyes -0.141748 0.185680 -0.763 0.4457
GFR_MDRD -0.002586 0.002983 -0.867 0.3866
BMI -0.021783 0.014365 -1.516 0.1303
MedHx_CVDyes 0.084021 0.110013 0.764 0.4455
stenose50-70% -0.356397 0.785845 -0.454 0.6504
stenose70-90% 0.217173 0.738647 0.294 0.7689
stenose90-99% 0.110102 0.736263 0.150 0.8812
stenose100% (Occlusion) -0.700863 0.912445 -0.768 0.4429
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.024 on 376 degrees of freedom
Multiple R-squared: 0.1437, Adjusted R-squared: 0.105
F-statistic: 3.712 on 17 and 376 DF, p-value: 1.282e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.075162
Standard error............: 0.054198
Odds ratio (effect size)..: 1.078
Lower 95% CI..............: 0.969
Upper 95% CI..............: 1.199
T-value...................: 1.386785
P-value...................: 0.166329
R^2.......................: 0.143706
Adjusted r^2..............: 0.104991
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + SmokerStatus + GFR_MDRD +
BMI + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
810.166924 0.126116 0.189062 -0.404478 -0.241479
SmokerStatusEx-smoker SmokerStatusNever smoked GFR_MDRD BMI stenose50-70%
-0.093546 0.430385 -0.004195 -0.020590 0.403433
stenose70-90% stenose90-99% stenose100% (Occlusion)
1.139604 0.905212 0.199254
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.72327 -0.66316 -0.08646 0.44284 2.87596
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 785.636195 117.607642 6.680 1.10e-10 ***
currentDF[, TRAIT] 0.118344 0.056769 2.085 0.0379 *
Age -0.006402 0.006929 -0.924 0.3562
Gendermale 0.176175 0.120604 1.461 0.1451
ORdate_year -0.391822 0.058697 -6.675 1.13e-10 ***
Hypertension.compositeyes -0.201754 0.167686 -1.203 0.2298
DiabetesStatusDiabetes 0.002035 0.135552 0.015 0.9880
SmokerStatusEx-smoker -0.056798 0.121106 -0.469 0.6394
SmokerStatusNever smoked 0.454896 0.193286 2.353 0.0192 *
Med.Statin.LLDyes -0.133265 0.125493 -1.062 0.2891
Med.all.antiplateletyes -0.206475 0.182994 -1.128 0.2601
GFR_MDRD -0.004745 0.002932 -1.619 0.1066
BMI -0.023108 0.015172 -1.523 0.1287
MedHx_CVDyes 0.017226 0.114878 0.150 0.8809
stenose50-70% 0.322200 1.025046 0.314 0.7535
stenose70-90% 1.060198 0.982079 1.080 0.2812
stenose90-99% 0.842752 0.980557 0.859 0.3907
stenose100% (Occlusion) -0.030354 1.105947 -0.027 0.9781
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9711 on 312 degrees of freedom
Multiple R-squared: 0.1998, Adjusted R-squared: 0.1562
F-statistic: 4.584 on 17 and 312 DF, p-value: 1.342e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.118344
Standard error............: 0.056769
Odds ratio (effect size)..: 1.126
Lower 95% CI..............: 1.007
Upper 95% CI..............: 1.258
T-value...................: 2.084657
P-value...................: 0.03791335
R^2.......................: 0.199846
Adjusted r^2..............: 0.156248
Sample size of AE DB......: 2423
Sample size of model......: 330
Missing data %............: 86.38052
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ ORdate_year + SmokerStatus,
data = currentDF)
Coefficients:
(Intercept) ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked
645.31534 -0.32218 -0.03792 0.31256
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8619 -0.7260 -0.1075 0.5228 2.9263
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 592.679846 106.270213 5.577 4.76e-08 ***
currentDF[, TRAIT] 0.071789 0.054685 1.313 0.190
Age -0.009345 0.006834 -1.367 0.172
Gendermale 0.146591 0.118981 1.232 0.219
ORdate_year -0.295222 0.053023 -5.568 5.00e-08 ***
Hypertension.compositeyes -0.126454 0.159970 -0.790 0.430
DiabetesStatusDiabetes 0.127121 0.134577 0.945 0.345
SmokerStatusEx-smoker 0.014106 0.119867 0.118 0.906
SmokerStatusNever smoked 0.408016 0.186167 2.192 0.029 *
Med.Statin.LLDyes -0.203236 0.124832 -1.628 0.104
Med.all.antiplateletyes -0.119314 0.190495 -0.626 0.531
GFR_MDRD -0.003591 0.003049 -1.178 0.240
BMI -0.018836 0.014826 -1.270 0.205
MedHx_CVDyes 0.092949 0.112288 0.828 0.408
stenose50-70% -0.374187 0.791057 -0.473 0.636
stenose70-90% 0.237446 0.746023 0.318 0.750
stenose90-99% 0.141648 0.743311 0.191 0.849
stenose100% (Occlusion) -0.743982 0.922429 -0.807 0.420
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.034 on 366 degrees of freedom
Multiple R-squared: 0.1435, Adjusted R-squared: 0.1037
F-statistic: 3.607 on 17 and 366 DF, p-value: 2.381e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.071789
Standard error............: 0.054685
Odds ratio (effect size)..: 1.074
Lower 95% CI..............: 0.965
Upper 95% CI..............: 1.196
T-value...................: 1.312776
P-value...................: 0.1900811
R^2.......................: 0.143501
Adjusted r^2..............: 0.103718
Sample size of AE DB......: 2423
Sample size of model......: 384
Missing data %............: 84.15188
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + SmokerStatus, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year SmokerStatusEx-smoker
724.86924 0.16736 0.20852 -0.36196 -0.06478
SmokerStatusNever smoked
0.32623
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0831 -0.7391 -0.1226 0.5069 3.2575
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 695.679535 106.750651 6.517 2.34e-10 ***
currentDF[, TRAIT] 0.149507 0.057274 2.610 0.00941 **
Age -0.008591 0.006597 -1.302 0.19361
Gendermale 0.214569 0.115132 1.864 0.06316 .
ORdate_year -0.346679 0.053255 -6.510 2.45e-10 ***
Hypertension.compositeyes -0.057844 0.160697 -0.360 0.71908
DiabetesStatusDiabetes 0.079644 0.130408 0.611 0.54175
SmokerStatusEx-smoker -0.008490 0.116642 -0.073 0.94201
SmokerStatusNever smoked 0.398002 0.180101 2.210 0.02772 *
Med.Statin.LLDyes -0.197215 0.119750 -1.647 0.10043
Med.all.antiplateletyes -0.191792 0.188834 -1.016 0.31045
GFR_MDRD -0.003148 0.002926 -1.076 0.28281
BMI -0.020220 0.014551 -1.390 0.16549
MedHx_CVDyes 0.109648 0.109604 1.000 0.31777
stenose50-70% -0.278791 0.774306 -0.360 0.71901
stenose70-90% 0.210242 0.731318 0.287 0.77390
stenose90-99% 0.140575 0.728495 0.193 0.84709
stenose100% (Occlusion) -0.767969 0.903227 -0.850 0.39573
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.012 on 371 degrees of freedom
Multiple R-squared: 0.1814, Adjusted R-squared: 0.1439
F-statistic: 4.836 on 17 and 371 DF, p-value: 2.173e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.149507
Standard error............: 0.057274
Odds ratio (effect size)..: 1.161
Lower 95% CI..............: 1.038
Upper 95% CI..............: 1.299
T-value...................: 2.610367
P-value...................: 0.009411002
R^2.......................: 0.181409
Adjusted r^2..............: 0.1439
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
SmokerStatus, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked
767.16792 0.16659 -0.38301 -0.02479 0.36109
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1160 -0.6899 -0.0858 0.5500 3.0575
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 744.468599 105.676361 7.045 9.08e-12 ***
currentDF[, TRAIT] 0.163288 0.055473 2.944 0.00345 **
Age -0.010047 0.006544 -1.535 0.12556
Gendermale 0.118678 0.116560 1.018 0.30926
ORdate_year -0.370709 0.052724 -7.031 9.91e-12 ***
Hypertension.compositeyes -0.155406 0.157686 -0.986 0.32500
DiabetesStatusDiabetes 0.111862 0.130719 0.856 0.39269
SmokerStatusEx-smoker 0.035606 0.116345 0.306 0.75975
SmokerStatusNever smoked 0.480194 0.178400 2.692 0.00743 **
Med.Statin.LLDyes -0.191829 0.119504 -1.605 0.10930
Med.all.antiplateletyes -0.119709 0.188315 -0.636 0.52537
GFR_MDRD -0.002809 0.002928 -0.959 0.33796
BMI -0.027942 0.014631 -1.910 0.05694 .
MedHx_CVDyes 0.117185 0.109192 1.073 0.28388
stenose50-70% -0.629163 0.777483 -0.809 0.41890
stenose70-90% -0.131383 0.736306 -0.178 0.85848
stenose90-99% -0.161700 0.731499 -0.221 0.82517
stenose100% (Occlusion) -1.143274 0.908480 -1.258 0.20902
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.01 on 371 degrees of freedom
Multiple R-squared: 0.1854, Adjusted R-squared: 0.1481
F-statistic: 4.967 on 17 and 371 DF, p-value: 1.028e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.163288
Standard error............: 0.055473
Odds ratio (effect size)..: 1.177
Lower 95% CI..............: 1.056
Upper 95% CI..............: 1.313
T-value...................: 2.943536
P-value...................: 0.003449031
R^2.......................: 0.185399
Adjusted r^2..............: 0.148072
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
SmokerStatus, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked
737.30920 0.09651 -0.36811 -0.03005 0.34068
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0105 -0.6901 -0.1152 0.5444 3.0285
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 713.167844 107.043941 6.662 9.75e-11 ***
currentDF[, TRAIT] 0.086810 0.053094 1.635 0.1029
Age -0.010253 0.006596 -1.554 0.1209
Gendermale 0.160678 0.116239 1.382 0.1677
ORdate_year -0.355233 0.053410 -6.651 1.04e-10 ***
Hypertension.compositeyes -0.117105 0.159192 -0.736 0.4624
DiabetesStatusDiabetes 0.091006 0.131542 0.692 0.4895
SmokerStatusEx-smoker 0.019955 0.117095 0.170 0.8648
SmokerStatusNever smoked 0.448745 0.179728 2.497 0.0130 *
Med.Statin.LLDyes -0.205003 0.120378 -1.703 0.0894 .
Med.all.antiplateletyes -0.158464 0.189399 -0.837 0.4033
GFR_MDRD -0.003108 0.002961 -1.050 0.2945
BMI -0.024938 0.014714 -1.695 0.0909 .
MedHx_CVDyes 0.126354 0.110017 1.148 0.2515
stenose50-70% -0.373104 0.778116 -0.479 0.6319
stenose70-90% 0.132526 0.735448 0.180 0.8571
stenose90-99% 0.055358 0.732581 0.076 0.9398
stenose100% (Occlusion) -0.852671 0.908718 -0.938 0.3487
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.018 on 371 degrees of freedom
Multiple R-squared: 0.1723, Adjusted R-squared: 0.1344
F-statistic: 4.544 on 17 and 371 DF, p-value: 1.158e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ug_2015_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ug_2015_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.08681
Standard error............: 0.053094
Odds ratio (effect size)..: 1.091
Lower 95% CI..............: 0.983
Upper 95% CI..............: 1.21
T-value...................: 1.635015
P-value...................: 0.102894
R^2.......................: 0.172338
Adjusted r^2..............: 0.134413
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
Analysis of MCP1_pg_ml_2015_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + ORdate_year +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) Age Gendermale ORdate_year SmokerStatusEx-smoker
392.542541 0.009152 0.248164 -0.196726 -0.242509
SmokerStatusNever smoked Med.Statin.LLDyes Med.all.antiplateletyes stenose50-70% stenose70-90%
0.095364 -0.178209 -0.310615 0.561873 1.110132
stenose90-99% stenose100% (Occlusion)
0.934465 -0.114191
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.86123 -0.57366 -0.03745 0.44256 2.66670
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.030e+02 1.121e+02 3.594 0.000381 ***
currentDF[, TRAIT] -6.417e-02 5.303e-02 -1.210 0.227275
Age 8.901e-03 6.581e-03 1.353 0.177186
Gendermale 2.308e-01 1.140e-01 2.026 0.043711 *
ORdate_year -2.020e-01 5.595e-02 -3.611 0.000358 ***
Hypertension.compositeyes -9.689e-02 1.563e-01 -0.620 0.535919
DiabetesStatusDiabetes 6.399e-02 1.311e-01 0.488 0.625777
SmokerStatusEx-smoker -2.236e-01 1.163e-01 -1.923 0.055473 .
SmokerStatusNever smoked 1.378e-01 1.836e-01 0.750 0.453757
Med.Statin.LLDyes -2.047e-01 1.163e-01 -1.760 0.079430 .
Med.all.antiplateletyes -3.047e-01 1.923e-01 -1.585 0.114035
GFR_MDRD 1.495e-03 2.969e-03 0.504 0.614915
BMI 2.149e-04 1.562e-02 0.014 0.989033
MedHx_CVDyes 1.169e-01 1.084e-01 1.078 0.281703
stenose50-70% 6.412e-01 9.428e-01 0.680 0.496954
stenose70-90% 1.221e+00 9.000e-01 1.357 0.175799
stenose90-99% 1.045e+00 8.985e-01 1.163 0.245840
stenose100% (Occlusion) 9.608e-03 1.061e+00 0.009 0.992780
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8844 on 295 degrees of freedom
Multiple R-squared: 0.1323, Adjusted R-squared: 0.08225
F-statistic: 2.645 on 17 and 295 DF, p-value: 0.0004977
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.064168
Standard error............: 0.053034
Odds ratio (effect size)..: 0.938
Lower 95% CI..............: 0.845
Upper 95% CI..............: 1.041
T-value...................: -1.209928
P-value...................: 0.227275
R^2.......................: 0.132252
Adjusted r^2..............: 0.082247
Sample size of AE DB......: 2423
Sample size of model......: 313
Missing data %............: 87.08213
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + SmokerStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
514.570221 -0.090736 0.008686 0.204460 -0.257261
SmokerStatusEx-smoker SmokerStatusNever smoked Med.Statin.LLDyes
-0.230470 0.023567 -0.192415
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.82607 -0.59893 -0.05389 0.47017 2.60324
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 488.844886 120.495720 4.057 6.51e-05 ***
currentDF[, TRAIT] -0.090320 0.055013 -1.642 0.1018
Age 0.007596 0.006748 1.126 0.2613
Gendermale 0.198266 0.118568 1.672 0.0956 .
ORdate_year -0.244476 0.060152 -4.064 6.32e-05 ***
Hypertension.compositeyes -0.091807 0.162354 -0.565 0.5722
DiabetesStatusDiabetes 0.074527 0.133376 0.559 0.5768
SmokerStatusEx-smoker -0.224830 0.117208 -1.918 0.0561 .
SmokerStatusNever smoked 0.088341 0.192119 0.460 0.6460
Med.Statin.LLDyes -0.216649 0.120270 -1.801 0.0728 .
Med.all.antiplateletyes -0.234374 0.200363 -1.170 0.2431
GFR_MDRD 0.000623 0.003205 0.194 0.8460
BMI -0.002501 0.015895 -0.157 0.8751
MedHx_CVDyes 0.124152 0.111365 1.115 0.2659
stenose70-90% 0.508215 0.299639 1.696 0.0910 .
stenose90-99% 0.414293 0.293921 1.410 0.1598
stenose100% (Occlusion) -0.569273 0.615725 -0.925 0.3560
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8698 on 271 degrees of freedom
Multiple R-squared: 0.1381, Adjusted R-squared: 0.08721
F-statistic: 2.714 on 16 and 271 DF, p-value: 0.000502
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.09032
Standard error............: 0.055013
Odds ratio (effect size)..: 0.914
Lower 95% CI..............: 0.82
Upper 95% CI..............: 1.018
T-value...................: -1.641809
P-value...................: 0.1017895
R^2.......................: 0.138099
Adjusted r^2..............: 0.087212
Sample size of AE DB......: 2423
Sample size of model......: 288
Missing data %............: 88.11391
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Med.Statin.LLD +
MedHx_CVD + stenose, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year Med.Statin.LLDyes MedHx_CVDyes stenose70-90%
454.8284 0.2136 -0.2275 -0.2701 0.1515 0.5440
stenose90-99% stenose100% (Occlusion)
0.4301 -0.2911
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8321 -0.5718 -0.0281 0.4503 2.4860
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.794e+02 1.132e+02 4.235 3.07e-05 ***
currentDF[, TRAIT] -5.024e-02 5.133e-02 -0.979 0.3285
Age 8.383e-03 6.514e-03 1.287 0.1991
Gendermale 2.335e-01 1.122e-01 2.082 0.0382 *
ORdate_year -2.398e-01 5.651e-02 -4.244 2.96e-05 ***
Hypertension.compositeyes -1.436e-01 1.526e-01 -0.941 0.3475
DiabetesStatusDiabetes -2.590e-03 1.303e-01 -0.020 0.9842
SmokerStatusEx-smoker -2.053e-01 1.132e-01 -1.814 0.0708 .
SmokerStatusNever smoked 7.838e-02 1.877e-01 0.418 0.6766
Med.Statin.LLDyes -2.196e-01 1.142e-01 -1.923 0.0555 .
Med.all.antiplateletyes -2.461e-01 1.943e-01 -1.267 0.2063
GFR_MDRD 6.336e-04 2.979e-03 0.213 0.8317
BMI 3.290e-03 1.498e-02 0.220 0.8263
MedHx_CVDyes 1.430e-01 1.062e-01 1.347 0.1790
stenose70-90% 4.690e-01 2.921e-01 1.606 0.1094
stenose90-99% 3.491e-01 2.893e-01 1.207 0.2284
stenose100% (Occlusion) -6.651e-01 6.098e-01 -1.091 0.2763
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8636 on 290 degrees of freedom
Multiple R-squared: 0.1351, Adjusted R-squared: 0.08733
F-statistic: 2.83 on 16 and 290 DF, p-value: 0.0002724
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.050244
Standard error............: 0.051331
Odds ratio (effect size)..: 0.951
Lower 95% CI..............: 0.86
Upper 95% CI..............: 1.052
T-value...................: -0.97881
P-value...................: 0.3284897
R^2.......................: 0.135052
Adjusted r^2..............: 0.087331
Sample size of AE DB......: 2423
Sample size of model......: 307
Missing data %............: 87.32976
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + SmokerStatus + Med.all.antiplatelet +
stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
536.89076 0.09124 0.01430 0.29158 -0.26864
SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes stenose50-70% stenose70-90%
-0.29724 0.00165 -0.25853 -0.21202 0.30343
stenose90-99% stenose100% (Occlusion)
0.13804 -0.79164
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.56234 -0.56044 -0.05685 0.44205 2.87297
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.149e+02 1.115e+02 4.618 5.76e-06 ***
currentDF[, TRAIT] 8.383e-02 5.097e-02 1.645 0.1010
Age 1.232e-02 6.825e-03 1.806 0.0720 .
Gendermale 2.905e-01 1.146e-01 2.534 0.0118 *
ORdate_year -2.574e-01 5.561e-02 -4.629 5.49e-06 ***
Hypertension.compositeyes -7.928e-02 1.587e-01 -0.499 0.6178
DiabetesStatusDiabetes 7.003e-02 1.327e-01 0.528 0.5982
SmokerStatusEx-smoker -2.770e-01 1.160e-01 -2.388 0.0176 *
SmokerStatusNever smoked 3.238e-02 1.867e-01 0.173 0.8625
Med.Statin.LLDyes -1.651e-01 1.163e-01 -1.419 0.1570
Med.all.antiplateletyes -2.393e-01 1.840e-01 -1.301 0.1944
GFR_MDRD -6.664e-04 3.069e-03 -0.217 0.8283
BMI -5.253e-03 1.457e-02 -0.360 0.7188
MedHx_CVDyes 8.727e-02 1.080e-01 0.808 0.4199
stenose50-70% -2.776e-01 6.977e-01 -0.398 0.6910
stenose70-90% 2.695e-01 6.507e-01 0.414 0.6790
stenose90-99% 9.198e-02 6.475e-01 0.142 0.8871
stenose100% (Occlusion) -8.701e-01 8.633e-01 -1.008 0.3144
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8929 on 298 degrees of freedom
Multiple R-squared: 0.1513, Adjusted R-squared: 0.1029
F-statistic: 3.126 on 17 and 298 DF, p-value: 4.06e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.083834
Standard error............: 0.050965
Odds ratio (effect size)..: 1.087
Lower 95% CI..............: 0.984
Upper 95% CI..............: 1.202
T-value...................: 1.644913
P-value...................: 0.1010421
R^2.......................: 0.151347
Adjusted r^2..............: 0.102934
Sample size of AE DB......: 2423
Sample size of model......: 316
Missing data %............: 86.95832
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes GFR_MDRD
630.898226 0.294590 0.249463 -0.314914 -0.172490 -0.004928
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.0692 -0.4832 -0.1142 0.4492 2.8441
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 633.387067 103.960623 6.093 3.54e-09 ***
currentDF[, TRAIT] 0.278350 0.051053 5.452 1.07e-07 ***
Age 0.001968 0.006444 0.305 0.7603
Gendermale 0.298238 0.110575 2.697 0.0074 **
ORdate_year -0.316106 0.051862 -6.095 3.49e-09 ***
Hypertension.compositeyes -0.089396 0.152294 -0.587 0.5577
DiabetesStatusDiabetes 0.074380 0.127177 0.585 0.5591
SmokerStatusEx-smoker -0.112578 0.109747 -1.026 0.3058
SmokerStatusNever smoked 0.162170 0.183369 0.884 0.3772
Med.Statin.LLDyes -0.183624 0.109619 -1.675 0.0950 .
Med.all.antiplateletyes -0.003682 0.176039 -0.021 0.9833
GFR_MDRD -0.004069 0.002787 -1.460 0.1453
BMI -0.011868 0.013765 -0.862 0.3893
MedHx_CVDyes 0.081132 0.103351 0.785 0.4331
stenose50-70% -0.339190 0.664109 -0.511 0.6099
stenose70-90% 0.156846 0.612840 0.256 0.7982
stenose90-99% 0.008115 0.610594 0.013 0.9894
stenose100% (Occlusion) -0.390377 0.792644 -0.492 0.6227
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8431 on 289 degrees of freedom
Multiple R-squared: 0.2311, Adjusted R-squared: 0.1859
F-statistic: 5.11 on 17 and 289 DF, p-value: 9.142e-10
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.27835
Standard error............: 0.051053
Odds ratio (effect size)..: 1.321
Lower 95% CI..............: 1.195
Upper 95% CI..............: 1.46
T-value...................: 5.452229
P-value...................: 1.067917e-07
R^2.......................: 0.231112
Adjusted r^2..............: 0.185884
Sample size of AE DB......: 2423
Sample size of model......: 307
Missing data %............: 87.32976
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus + Med.Statin.LLD + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes
384.488719 0.093561 0.317187 -0.191987 0.202138 -0.236999
GFR_MDRD
-0.003788
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.86228 -0.56593 -0.09133 0.44977 2.87992
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.976e+02 9.510e+01 4.181 3.72e-05 ***
currentDF[, TRAIT] 8.820e-02 5.159e-02 1.709 0.08832 .
Age -8.335e-04 6.525e-03 -0.128 0.89844
Gendermale 3.226e-01 1.126e-01 2.866 0.00443 **
ORdate_year -1.982e-01 4.745e-02 -4.176 3.80e-05 ***
Hypertension.compositeyes -1.557e-01 1.555e-01 -1.002 0.31720
DiabetesStatusDiabetes 2.314e-01 1.333e-01 1.736 0.08349 .
SmokerStatusEx-smoker -6.849e-02 1.152e-01 -0.595 0.55256
SmokerStatusNever smoked 7.397e-02 1.718e-01 0.431 0.66704
Med.Statin.LLDyes -2.489e-01 1.186e-01 -2.100 0.03652 *
Med.all.antiplateletyes -2.231e-01 1.892e-01 -1.179 0.23912
GFR_MDRD -3.231e-03 2.829e-03 -1.142 0.25419
BMI -1.787e-02 1.389e-02 -1.286 0.19920
MedHx_CVDyes 1.148e-01 1.073e-01 1.069 0.28566
stenose50-70% -2.741e-01 7.202e-01 -0.381 0.70378
stenose70-90% 1.169e-01 6.716e-01 0.174 0.86195
stenose90-99% 3.895e-02 6.685e-01 0.058 0.95357
stenose100% (Occlusion) -8.265e-01 8.305e-01 -0.995 0.32034
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9278 on 328 degrees of freedom
Multiple R-squared: 0.1322, Adjusted R-squared: 0.08727
F-statistic: 2.94 on 17 and 328 DF, p-value: 9.979e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.088196
Standard error............: 0.051594
Odds ratio (effect size)..: 1.092
Lower 95% CI..............: 0.987
Upper 95% CI..............: 1.208
T-value...................: 1.709435
P-value...................: 0.08831622
R^2.......................: 0.132246
Adjusted r^2..............: 0.087271
Sample size of AE DB......: 2423
Sample size of model......: 346
Missing data %............: 85.72018
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + SmokerStatus +
Med.Statin.LLD + Med.all.antiplatelet + stenose, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year SmokerStatusEx-smoker SmokerStatusNever smoked
476.6969 0.3218 -0.2382 -0.2260 0.1043
Med.Statin.LLDyes Med.all.antiplateletyes stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.2430 -0.3095 0.4474 0.3865 -0.7015
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.74045 -0.60013 0.00664 0.43651 2.50449
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.916e+02 1.294e+02 3.798 0.000182 ***
currentDF[, TRAIT] -4.521e-02 5.845e-02 -0.773 0.439964
Age 6.254e-03 7.019e-03 0.891 0.373785
Gendermale 3.129e-01 1.224e-01 2.556 0.011150 *
ORdate_year -2.457e-01 6.458e-02 -3.804 0.000177 ***
Hypertension.compositeyes -1.539e-01 1.699e-01 -0.906 0.365744
DiabetesStatusDiabetes 5.404e-02 1.404e-01 0.385 0.700726
SmokerStatusEx-smoker -2.513e-01 1.215e-01 -2.068 0.039665 *
SmokerStatusNever smoked 1.117e-01 1.984e-01 0.563 0.573674
Med.Statin.LLDyes -2.335e-01 1.229e-01 -1.900 0.058569 .
Med.all.antiplateletyes -2.998e-01 2.088e-01 -1.435 0.152359
GFR_MDRD -1.148e-03 3.309e-03 -0.347 0.728890
BMI 2.322e-04 1.607e-02 0.014 0.988481
MedHx_CVDyes 6.599e-02 1.178e-01 0.560 0.575865
stenose70-90% 4.422e-01 3.196e-01 1.384 0.167585
stenose90-99% 3.663e-01 3.147e-01 1.164 0.245494
stenose100% (Occlusion) -7.035e-01 6.363e-01 -1.106 0.269961
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8824 on 258 degrees of freedom
Multiple R-squared: 0.1392, Adjusted R-squared: 0.08582
F-statistic: 2.608 on 16 and 258 DF, p-value: 0.0008597
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.045209
Standard error............: 0.058451
Odds ratio (effect size)..: 0.956
Lower 95% CI..............: 0.852
Upper 95% CI..............: 1.072
T-value...................: -0.773451
P-value...................: 0.4399637
R^2.......................: 0.139206
Adjusted r^2..............: 0.085824
Sample size of AE DB......: 2423
Sample size of model......: 275
Missing data %............: 88.65043
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + ORdate_year +
SmokerStatus, data = currentDF)
Coefficients:
(Intercept) Age Gendermale ORdate_year SmokerStatusEx-smoker
532.77260 0.01229 0.26064 -0.26656 -0.25418
SmokerStatusNever smoked
0.06294
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7378 -0.5775 -0.0569 0.4343 2.5812
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.162e+02 1.229e+02 4.198 3.65e-05 ***
currentDF[, TRAIT] -6.332e-02 5.563e-02 -1.138 0.2561
Age 8.586e-03 6.886e-03 1.247 0.2135
Gendermale 2.577e-01 1.175e-01 2.194 0.0291 *
ORdate_year -2.581e-01 6.135e-02 -4.207 3.53e-05 ***
Hypertension.compositeyes -1.674e-01 1.644e-01 -1.018 0.3097
DiabetesStatusDiabetes 8.757e-02 1.350e-01 0.648 0.5172
SmokerStatusEx-smoker -2.363e-01 1.203e-01 -1.964 0.0506 .
SmokerStatusNever smoked 1.287e-01 1.867e-01 0.689 0.4913
Med.Statin.LLDyes -1.785e-01 1.199e-01 -1.488 0.1379
Med.all.antiplateletyes -2.245e-01 2.009e-01 -1.118 0.2647
GFR_MDRD -1.184e-03 3.189e-03 -0.371 0.7107
BMI -1.466e-04 1.592e-02 -0.009 0.9927
MedHx_CVDyes 7.989e-02 1.126e-01 0.709 0.4788
stenose70-90% 4.777e-01 2.990e-01 1.598 0.1112
stenose90-99% 3.531e-01 2.944e-01 1.199 0.2315
stenose100% (Occlusion) -6.713e-01 7.179e-01 -0.935 0.3506
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8686 on 270 degrees of freedom
Multiple R-squared: 0.1387, Adjusted R-squared: 0.08761
F-statistic: 2.716 on 16 and 270 DF, p-value: 0.0004971
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.063318
Standard error............: 0.055632
Odds ratio (effect size)..: 0.939
Lower 95% CI..............: 0.842
Upper 95% CI..............: 1.047
T-value...................: -1.138172
P-value...................: 0.2560576
R^2.......................: 0.138651
Adjusted r^2..............: 0.087608
Sample size of AE DB......: 2423
Sample size of model......: 287
Missing data %............: 88.15518
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
411.04632 0.10993 0.25064 -0.20526 -0.17670 -0.25620
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.37642 0.14323 0.01004 -0.77295
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.84779 -0.58825 -0.09605 0.49423 2.90353
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 415.427878 90.309630 4.600 5.79e-06 ***
currentDF[, TRAIT] 0.113108 0.048211 2.346 0.01949 *
Age 0.002954 0.005968 0.495 0.62094
Gendermale 0.267744 0.102927 2.601 0.00966 **
ORdate_year -0.207271 0.045061 -4.600 5.79e-06 ***
Hypertension.compositeyes -0.037186 0.139942 -0.266 0.79060
DiabetesStatusDiabetes 0.123337 0.117763 1.047 0.29562
SmokerStatusEx-smoker -0.108876 0.104663 -1.040 0.29889
SmokerStatusNever smoked 0.023734 0.161413 0.147 0.88318
Med.Statin.LLDyes -0.184569 0.108450 -1.702 0.08961 .
Med.all.antiplateletyes -0.224056 0.164980 -1.358 0.17525
GFR_MDRD -0.002470 0.002644 -0.934 0.35086
BMI -0.014774 0.012759 -1.158 0.24763
MedHx_CVDyes 0.077430 0.097591 0.793 0.42804
stenose50-70% -0.374935 0.698676 -0.537 0.59184
stenose70-90% 0.134328 0.657326 0.204 0.83819
stenose90-99% -0.015391 0.655487 -0.023 0.98128
stenose100% (Occlusion) -0.784723 0.809827 -0.969 0.33317
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.909 on 375 degrees of freedom
Multiple R-squared: 0.1231, Adjusted R-squared: 0.08333
F-statistic: 3.096 on 17 and 375 DF, p-value: 3.899e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.113108
Standard error............: 0.048211
Odds ratio (effect size)..: 1.12
Lower 95% CI..............: 1.019
Upper 95% CI..............: 1.231
T-value...................: 2.346125
P-value...................: 0.01948982
R^2.......................: 0.123083
Adjusted r^2..............: 0.083329
Sample size of AE DB......: 2423
Sample size of model......: 393
Missing data %............: 83.78044
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
405.182671 0.104808 0.244204 -0.202331 -0.179626 -0.251704
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.388887 0.136948 0.004196 -0.802007
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.83602 -0.60273 -0.08754 0.48268 2.91794
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 409.015054 90.055759 4.542 7.52e-06 ***
currentDF[, TRAIT] 0.106463 0.048741 2.184 0.0296 *
Age 0.002770 0.005937 0.467 0.6411
Gendermale 0.260558 0.102780 2.535 0.0116 *
ORdate_year -0.204048 0.044933 -4.541 7.54e-06 ***
Hypertension.compositeyes -0.043645 0.139743 -0.312 0.7550
DiabetesStatusDiabetes 0.114297 0.117547 0.972 0.3315
SmokerStatusEx-smoker -0.102813 0.104408 -0.985 0.3254
SmokerStatusNever smoked 0.025817 0.161324 0.160 0.8729
Med.Statin.LLDyes -0.188168 0.108176 -1.739 0.0828 .
Med.all.antiplateletyes -0.220278 0.164886 -1.336 0.1824
GFR_MDRD -0.002499 0.002643 -0.946 0.3449
BMI -0.015318 0.012747 -1.202 0.2302
MedHx_CVDyes 0.077591 0.097267 0.798 0.4255
stenose50-70% -0.388909 0.699121 -0.556 0.5783
stenose70-90% 0.128290 0.657599 0.195 0.8454
stenose90-99% -0.019985 0.655858 -0.030 0.9757
stenose100% (Occlusion) -0.820034 0.809930 -1.012 0.3120
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9088 on 376 degrees of freedom
Multiple R-squared: 0.1213, Adjusted R-squared: 0.0816
F-statistic: 3.054 on 17 and 376 DF, p-value: 4.895e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.106463
Standard error............: 0.048741
Odds ratio (effect size)..: 1.112
Lower 95% CI..............: 1.011
Upper 95% CI..............: 1.224
T-value...................: 2.184252
P-value...................: 0.02956092
R^2.......................: 0.121324
Adjusted r^2..............: 0.081597
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year SmokerStatusEx-smoker
621.99368 -0.09008 0.32069 -0.31052 -0.16285
SmokerStatusNever smoked Med.Statin.LLDyes Med.all.antiplateletyes
0.16166 -0.23576 -0.26283
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.71445 -0.58797 -0.02635 0.45200 2.75578
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 612.052639 117.722658 5.199 3.80e-07 ***
currentDF[, TRAIT] -0.080391 0.060445 -1.330 0.18458
Age 0.005485 0.006683 0.821 0.41247
Gendermale 0.329615 0.116534 2.828 0.00501 **
ORdate_year -0.305579 0.058730 -5.203 3.73e-07 ***
Hypertension.compositeyes -0.120130 0.160966 -0.746 0.45609
DiabetesStatusDiabetes 0.089243 0.131354 0.679 0.49742
SmokerStatusEx-smoker -0.202089 0.115821 -1.745 0.08208 .
SmokerStatusNever smoked 0.181199 0.187965 0.964 0.33585
Med.Statin.LLDyes -0.239536 0.120075 -1.995 0.04700 *
Med.all.antiplateletyes -0.284206 0.183541 -1.548 0.12261
GFR_MDRD -0.001487 0.002996 -0.496 0.61998
BMI -0.008408 0.014307 -0.588 0.55720
MedHx_CVDyes 0.058302 0.111399 0.523 0.60112
stenose50-70% -0.252475 0.707159 -0.357 0.72133
stenose70-90% 0.173170 0.649749 0.267 0.79003
stenose90-99% 0.048313 0.645777 0.075 0.94041
stenose100% (Occlusion) -1.049197 0.928350 -1.130 0.25934
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8866 on 288 degrees of freedom
Multiple R-squared: 0.163, Adjusted R-squared: 0.1136
F-statistic: 3.3 on 17 and 288 DF, p-value: 1.674e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.080391
Standard error............: 0.060445
Odds ratio (effect size)..: 0.923
Lower 95% CI..............: 0.82
Upper 95% CI..............: 1.039
T-value...................: -1.329984
P-value...................: 0.1845759
R^2.......................: 0.163039
Adjusted r^2..............: 0.113635
Sample size of AE DB......: 2423
Sample size of model......: 306
Missing data %............: 87.37103
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Med.Statin.LLD,
data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year Med.Statin.LLDyes
556.5560 0.2372 -0.2780 -0.1648
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7754 -0.5489 -0.0152 0.4509 2.5088
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.433e+02 1.232e+02 4.412 1.50e-05 ***
currentDF[, TRAIT] -3.268e-02 5.481e-02 -0.596 0.5516
Age 7.883e-03 6.839e-03 1.153 0.2501
Gendermale 2.726e-01 1.186e-01 2.298 0.0223 *
ORdate_year -2.718e-01 6.148e-02 -4.420 1.44e-05 ***
Hypertension.compositeyes -1.085e-01 1.638e-01 -0.662 0.5084
DiabetesStatusDiabetes -4.051e-02 1.357e-01 -0.299 0.7655
SmokerStatusEx-smoker -1.902e-01 1.203e-01 -1.582 0.1149
SmokerStatusNever smoked 6.383e-02 1.923e-01 0.332 0.7402
Med.Statin.LLDyes -1.618e-01 1.197e-01 -1.352 0.1776
Med.all.antiplateletyes -1.498e-01 2.050e-01 -0.730 0.4658
GFR_MDRD -5.336e-04 3.171e-03 -0.168 0.8665
BMI 2.839e-03 1.606e-02 0.177 0.8598
MedHx_CVDyes 8.708e-02 1.144e-01 0.761 0.4472
stenose70-90% 4.288e-01 2.998e-01 1.430 0.1539
stenose90-99% 3.476e-01 2.943e-01 1.181 0.2387
stenose100% (Occlusion) -5.880e-01 7.201e-01 -0.817 0.4149
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8656 on 262 degrees of freedom
Multiple R-squared: 0.1332, Adjusted R-squared: 0.08031
F-statistic: 2.517 on 16 and 262 DF, p-value: 0.001306
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TNFA_rank
Effect size...............: -0.032677
Standard error............: 0.05481
Odds ratio (effect size)..: 0.968
Lower 95% CI..............: 0.869
Upper 95% CI..............: 1.078
T-value...................: -0.596184
P-value...................: 0.5515672
R^2.......................: 0.133239
Adjusted r^2..............: 0.080308
Sample size of AE DB......: 2423
Sample size of model......: 279
Missing data %............: 88.48535
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
327.59797 0.08919 0.25491 -0.16366 -0.18181 -0.25784
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.27839 0.23342 0.09565 -0.73167
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.73867 -0.57017 -0.07035 0.47988 3.01508
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 338.199890 97.550336 3.467 0.000587 ***
currentDF[, TRAIT] 0.087501 0.053520 1.635 0.102901
Age 0.001857 0.005921 0.314 0.753939
Gendermale 0.269380 0.102894 2.618 0.009201 **
ORdate_year -0.168710 0.048674 -3.466 0.000589 ***
Hypertension.compositeyes -0.038802 0.140333 -0.277 0.782315
DiabetesStatusDiabetes 0.129309 0.118727 1.089 0.276795
SmokerStatusEx-smoker -0.070581 0.104977 -0.672 0.501775
SmokerStatusNever smoked 0.079068 0.160842 0.492 0.623297
Med.Statin.LLDyes -0.192336 0.108491 -1.773 0.077065 .
Med.all.antiplateletyes -0.229251 0.165815 -1.383 0.167619
GFR_MDRD -0.002235 0.002668 -0.838 0.402740
BMI -0.017199 0.012801 -1.344 0.179884
MedHx_CVDyes 0.072400 0.097705 0.741 0.459152
stenose50-70% -0.308804 0.699305 -0.442 0.659043
stenose70-90% 0.200661 0.657685 0.305 0.760457
stenose90-99% 0.051597 0.655860 0.079 0.937337
stenose100% (Occlusion) -0.787362 0.811876 -0.970 0.332767
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9113 on 376 degrees of freedom
Multiple R-squared: 0.1165, Adjusted R-squared: 0.07651
F-statistic: 2.915 on 17 and 376 DF, p-value: 0.0001035
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.087501
Standard error............: 0.05352
Odds ratio (effect size)..: 1.091
Lower 95% CI..............: 0.983
Upper 95% CI..............: 1.212
T-value...................: 1.634929
P-value...................: 0.1029007
R^2.......................: 0.116456
Adjusted r^2..............: 0.076509
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes GFR_MDRD
276.033577 0.232392 0.233467 -0.137843 -0.150335 -0.004144
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.05653 -0.61057 -0.04983 0.49520 2.92997
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 294.072490 89.527059 3.285 0.00112 **
currentDF[, TRAIT] 0.229772 0.045925 5.003 8.71e-07 ***
Age 0.002554 0.005791 0.441 0.65949
Gendermale 0.236517 0.100829 2.346 0.01951 *
ORdate_year -0.146876 0.044663 -3.289 0.00110 **
Hypertension.compositeyes -0.014016 0.136176 -0.103 0.91808
DiabetesStatusDiabetes 0.166272 0.115477 1.440 0.15075
SmokerStatusEx-smoker -0.081742 0.101723 -0.804 0.42216
SmokerStatusNever smoked 0.024922 0.156109 0.160 0.87325
Med.Statin.LLDyes -0.162972 0.105611 -1.543 0.12365
Med.all.antiplateletyes -0.180003 0.164625 -1.093 0.27492
GFR_MDRD -0.002865 0.002574 -1.113 0.26635
BMI -0.010551 0.012479 -0.845 0.39840
MedHx_CVDyes 0.057482 0.095203 0.604 0.54635
stenose50-70% -0.170783 0.676686 -0.252 0.80089
stenose70-90% 0.315509 0.635829 0.496 0.62003
stenose90-99% 0.208862 0.633630 0.330 0.74187
stenose100% (Occlusion) -0.388750 0.788899 -0.493 0.62246
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8819 on 372 degrees of freedom
Multiple R-squared: 0.1648, Adjusted R-squared: 0.1266
F-statistic: 4.318 on 17 and 372 DF, p-value: 4.187e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 0.229772
Standard error............: 0.045925
Odds ratio (effect size)..: 1.258
Lower 95% CI..............: 1.15
Upper 95% CI..............: 1.377
T-value...................: 5.003229
P-value...................: 8.711731e-07
R^2.......................: 0.164813
Adjusted r^2..............: 0.126645
Sample size of AE DB......: 2423
Sample size of model......: 390
Missing data %............: 83.90425
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes
390.8669 0.1196 0.2838 -0.1953 0.1815 -0.2409
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.85101 -0.60103 -0.09931 0.46756 2.87469
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.936e+02 9.324e+01 4.221 3.12e-05 ***
currentDF[, TRAIT] 1.233e-01 5.143e-02 2.398 0.01703 *
Age 1.084e-04 6.371e-03 0.017 0.98644
Gendermale 2.983e-01 1.109e-01 2.691 0.00749 **
ORdate_year -1.962e-01 4.653e-02 -4.217 3.17e-05 ***
Hypertension.compositeyes -1.460e-01 1.499e-01 -0.974 0.33074
DiabetesStatusDiabetes 1.943e-01 1.267e-01 1.533 0.12620
SmokerStatusEx-smoker -1.072e-01 1.123e-01 -0.955 0.34044
SmokerStatusNever smoked 3.720e-02 1.686e-01 0.221 0.82549
Med.Statin.LLDyes -2.502e-01 1.158e-01 -2.160 0.03150 *
Med.all.antiplateletyes -1.973e-01 1.841e-01 -1.072 0.28457
GFR_MDRD -2.464e-03 2.747e-03 -0.897 0.37043
BMI -1.472e-02 1.340e-02 -1.099 0.27257
MedHx_CVDyes 9.074e-02 1.049e-01 0.865 0.38748
stenose50-70% -3.553e-01 7.163e-01 -0.496 0.62020
stenose70-90% 3.845e-02 6.682e-01 0.058 0.95415
stenose90-99% -5.637e-02 6.657e-01 -0.085 0.93256
stenose100% (Occlusion) -8.718e-01 8.247e-01 -1.057 0.29121
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9209 on 339 degrees of freedom
Multiple R-squared: 0.1316, Adjusted R-squared: 0.0881
F-statistic: 3.023 on 17 and 339 DF, p-value: 6.264e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.123314
Standard error............: 0.051427
Odds ratio (effect size)..: 1.131
Lower 95% CI..............: 1.023
Upper 95% CI..............: 1.251
T-value...................: 2.397858
P-value...................: 0.0170316
R^2.......................: 0.131645
Adjusted r^2..............: 0.088099
Sample size of AE DB......: 2423
Sample size of model......: 357
Missing data %............: 85.2662
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes GFR_MDRD
305.057176 0.094953 0.262233 -0.152326 -0.181767 -0.004042
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.71821 -0.56090 -0.08476 0.48422 2.96589
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 316.791237 95.783310 3.307 0.00103 **
currentDF[, TRAIT] 0.109248 0.050329 2.171 0.03059 *
Age 0.001929 0.005936 0.325 0.74536
Gendermale 0.262922 0.104073 2.526 0.01194 *
ORdate_year -0.158012 0.047789 -3.306 0.00104 **
Hypertension.compositeyes -0.110746 0.141509 -0.783 0.43436
DiabetesStatusDiabetes 0.170591 0.119654 1.426 0.15480
SmokerStatusEx-smoker -0.058091 0.104846 -0.554 0.57987
SmokerStatusNever smoked 0.080405 0.160410 0.501 0.61650
Med.Statin.LLDyes -0.199272 0.108726 -1.833 0.06764 .
Med.all.antiplateletyes -0.176983 0.169594 -1.044 0.29737
GFR_MDRD -0.003006 0.002650 -1.135 0.25729
BMI -0.017358 0.012826 -1.353 0.17676
MedHx_CVDyes 0.083674 0.098586 0.849 0.39658
stenose50-70% -0.274462 0.698422 -0.393 0.69456
stenose70-90% 0.197684 0.653277 0.303 0.76236
stenose90-99% 0.102251 0.650674 0.157 0.87522
stenose100% (Occlusion) -0.839014 0.809301 -1.037 0.30055
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9056 on 369 degrees of freedom
Multiple R-squared: 0.1196, Adjusted R-squared: 0.07904
F-statistic: 2.949 on 17 and 369 DF, p-value: 8.759e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.109248
Standard error............: 0.050329
Odds ratio (effect size)..: 1.115
Lower 95% CI..............: 1.011
Upper 95% CI..............: 1.231
T-value...................: 2.170697
P-value...................: 0.03059111
R^2.......................: 0.119599
Adjusted r^2..............: 0.079039
Sample size of AE DB......: 2423
Sample size of model......: 387
Missing data %............: 84.02806
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + Med.Statin.LLD +
Med.all.antiplatelet + stenose, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes stenose50-70%
397.1635 0.2619 -0.1984 -0.1993 -0.2726 -0.2302
stenose70-90% stenose90-99% stenose100% (Occlusion)
0.2540 0.1541 -0.7695
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.83555 -0.57764 -0.07849 0.47187 2.93866
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 433.402151 95.084317 4.558 7.05e-06 ***
currentDF[, TRAIT] 0.057170 0.051539 1.109 0.2680
Age 0.001490 0.006128 0.243 0.8080
Gendermale 0.267166 0.105118 2.542 0.0114 *
ORdate_year -0.216152 0.047444 -4.556 7.12e-06 ***
Hypertension.compositeyes -0.083627 0.143210 -0.584 0.5596
DiabetesStatusDiabetes 0.149945 0.121110 1.238 0.2165
SmokerStatusEx-smoker -0.088808 0.107050 -0.830 0.4073
SmokerStatusNever smoked 0.026311 0.166229 0.158 0.8743
Med.Statin.LLDyes -0.207232 0.110477 -1.876 0.0615 .
Med.all.antiplateletyes -0.251581 0.169125 -1.488 0.1377
GFR_MDRD -0.002510 0.002682 -0.936 0.3500
BMI -0.015311 0.012951 -1.182 0.2379
MedHx_CVDyes 0.050956 0.100224 0.508 0.6115
stenose50-70% -0.332955 0.708961 -0.470 0.6389
stenose70-90% 0.149226 0.663405 0.225 0.8222
stenose90-99% 0.036439 0.661283 0.055 0.9561
stenose100% (Occlusion) -0.870780 0.818548 -1.064 0.2881
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9165 on 365 degrees of freedom
Multiple R-squared: 0.1144, Adjusted R-squared: 0.07314
F-statistic: 2.773 on 17 and 365 DF, p-value: 0.0002245
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.05717
Standard error............: 0.051539
Odds ratio (effect size)..: 1.059
Lower 95% CI..............: 0.957
Upper 95% CI..............: 1.171
T-value...................: 1.109267
P-value...................: 0.2680453
R^2.......................: 0.114389
Adjusted r^2..............: 0.073141
Sample size of AE DB......: 2423
Sample size of model......: 383
Missing data %............: 84.19315
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus + Med.Statin.LLD + MedHx_CVD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes
394.1860 0.1118 0.2846 -0.1970 0.1867 -0.2111
MedHx_CVDyes
0.1530
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.80479 -0.55718 -0.07259 0.49249 2.92738
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 392.264600 93.612243 4.190 3.58e-05 ***
currentDF[, TRAIT] 0.114320 0.050694 2.255 0.02478 *
Age 0.001696 0.006430 0.264 0.79215
Gendermale 0.291645 0.109263 2.669 0.00798 **
ORdate_year -0.195688 0.046707 -4.190 3.58e-05 ***
Hypertension.compositeyes -0.096244 0.148954 -0.646 0.51864
DiabetesStatusDiabetes 0.198566 0.127435 1.558 0.12014
SmokerStatusEx-smoker -0.092502 0.112632 -0.821 0.41208
SmokerStatusNever smoked -0.060865 0.175315 -0.347 0.72868
Med.Statin.LLDyes -0.221663 0.114899 -1.929 0.05456 .
Med.all.antiplateletyes -0.178895 0.173723 -1.030 0.30387
GFR_MDRD -0.002547 0.002792 -0.912 0.36224
BMI -0.015900 0.013497 -1.178 0.23962
MedHx_CVDyes 0.150332 0.104125 1.444 0.14975
stenose50-70% -0.328698 0.704867 -0.466 0.64129
stenose70-90% 0.119639 0.660407 0.181 0.85635
stenose90-99% 0.012923 0.657247 0.020 0.98432
stenose100% (Occlusion) -0.687557 0.815708 -0.843 0.39989
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9122 on 332 degrees of freedom
Multiple R-squared: 0.1314, Adjusted R-squared: 0.08696
F-statistic: 2.955 on 17 and 332 DF, p-value: 9.137e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.11432
Standard error............: 0.050694
Odds ratio (effect size)..: 1.121
Lower 95% CI..............: 1.015
Upper 95% CI..............: 1.238
T-value...................: 2.255113
P-value...................: 0.02477717
R^2.......................: 0.131437
Adjusted r^2..............: 0.086963
Sample size of AE DB......: 2423
Sample size of model......: 350
Missing data %............: 85.5551
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
414.66554 0.08220 0.24220 -0.20708 -0.17596 -0.24872
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.36331 0.16201 0.03217 -0.80655
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.87499 -0.58734 -0.07708 0.44780 2.97087
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 417.498390 90.931970 4.591 6.02e-06 ***
currentDF[, TRAIT] 0.080481 0.050107 1.606 0.1091
Age 0.002049 0.005935 0.345 0.7301
Gendermale 0.260287 0.103599 2.512 0.0124 *
ORdate_year -0.208268 0.045369 -4.591 6.04e-06 ***
Hypertension.compositeyes -0.050677 0.140089 -0.362 0.7177
DiabetesStatusDiabetes 0.110263 0.117855 0.936 0.3501
SmokerStatusEx-smoker -0.099123 0.104723 -0.947 0.3445
SmokerStatusNever smoked 0.033779 0.162004 0.209 0.8349
Med.Statin.LLDyes -0.185243 0.108516 -1.707 0.0886 .
Med.all.antiplateletyes -0.218136 0.165403 -1.319 0.1880
GFR_MDRD -0.002504 0.002653 -0.944 0.3458
BMI -0.015520 0.012784 -1.214 0.2255
MedHx_CVDyes 0.077145 0.097579 0.791 0.4297
stenose50-70% -0.364847 0.701528 -0.520 0.6033
stenose70-90% 0.154109 0.659930 0.234 0.8155
stenose90-99% 0.010521 0.658143 0.016 0.9873
stenose100% (Occlusion) -0.828383 0.812917 -1.019 0.3088
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9114 on 376 degrees of freedom
Multiple R-squared: 0.1162, Adjusted R-squared: 0.07628
F-statistic: 2.909 on 17 and 376 DF, p-value: 0.0001069
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.080481
Standard error............: 0.050107
Odds ratio (effect size)..: 1.084
Lower 95% CI..............: 0.982
Upper 95% CI..............: 1.196
T-value...................: 1.606182
P-value...................: 0.1090734
R^2.......................: 0.116239
Adjusted r^2..............: 0.076281
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
237.723792 0.128988 0.237129 -0.118596 -0.202177
Med.Statin.LLDyes GFR_MDRD
-0.233406 -0.004844
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.7881 -0.5603 -0.1068 0.4896 2.7203
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.632e+02 1.113e+02 2.364 0.0187 *
currentDF[, TRAIT] 1.240e-01 5.316e-02 2.333 0.0203 *
Age 1.261e-04 6.320e-03 0.020 0.9841
Gendermale 2.380e-01 1.104e-01 2.156 0.0318 *
ORdate_year -1.310e-01 5.556e-02 -2.358 0.0190 *
Hypertension.compositeyes -2.000e-01 1.487e-01 -1.345 0.1796
DiabetesStatusDiabetes 1.425e-01 1.279e-01 1.114 0.2662
SmokerStatusEx-smoker -3.534e-02 1.133e-01 -0.312 0.7553
SmokerStatusNever smoked 6.836e-02 1.678e-01 0.408 0.6839
Med.Statin.LLDyes -2.679e-01 1.194e-01 -2.243 0.0256 *
Med.all.antiplateletyes -1.227e-01 1.787e-01 -0.687 0.4928
GFR_MDRD -4.296e-03 2.912e-03 -1.475 0.1412
BMI -2.046e-02 1.398e-02 -1.464 0.1443
MedHx_CVDyes 3.161e-02 1.064e-01 0.297 0.7667
stenose50-70% -3.162e-01 7.028e-01 -0.450 0.6531
stenose70-90% 1.214e-01 6.552e-01 0.185 0.8531
stenose90-99% 1.514e-02 6.531e-01 0.023 0.9815
stenose100% (Occlusion) -7.670e-01 8.121e-01 -0.945 0.3456
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9061 on 318 degrees of freedom
Multiple R-squared: 0.108, Adjusted R-squared: 0.06026
F-statistic: 2.264 on 17 and 318 DF, p-value: 0.00319
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.124034
Standard error............: 0.053158
Odds ratio (effect size)..: 1.132
Lower 95% CI..............: 1.02
Upper 95% CI..............: 1.256
T-value...................: 2.333299
P-value...................: 0.02025553
R^2.......................: 0.107952
Adjusted r^2..............: 0.060264
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes
312.9352 0.1300 0.2676 -0.1564 -0.1703
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.76275 -0.56777 -0.06522 0.49499 3.06302
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 334.699245 94.671396 3.535 0.000458 ***
currentDF[, TRAIT] 0.109715 0.050424 2.176 0.030190 *
Age 0.001597 0.005887 0.271 0.786364
Gendermale 0.282247 0.102195 2.762 0.006030 **
ORdate_year -0.167046 0.047225 -3.537 0.000455 ***
Hypertension.compositeyes -0.022396 0.140390 -0.160 0.873339
DiabetesStatusDiabetes 0.130251 0.118046 1.103 0.270563
SmokerStatusEx-smoker -0.083711 0.104194 -0.803 0.422246
SmokerStatusNever smoked 0.053518 0.160351 0.334 0.738749
Med.Statin.LLDyes -0.191845 0.108187 -1.773 0.076993 .
Med.all.antiplateletyes -0.182511 0.165129 -1.105 0.269751
GFR_MDRD -0.002411 0.002645 -0.911 0.362630
BMI -0.015971 0.012744 -1.253 0.210910
MedHx_CVDyes 0.074556 0.097314 0.766 0.444079
stenose50-70% -0.169455 0.698458 -0.243 0.808437
stenose70-90% 0.300234 0.655380 0.458 0.647140
stenose90-99% 0.157672 0.652982 0.241 0.809327
stenose100% (Occlusion) -0.581800 0.813639 -0.715 0.475017
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9088 on 376 degrees of freedom
Multiple R-squared: 0.1212, Adjusted R-squared: 0.08151
F-statistic: 3.051 on 17 and 376 DF, p-value: 4.959e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.109715
Standard error............: 0.050424
Odds ratio (effect size)..: 1.116
Lower 95% CI..............: 1.011
Upper 95% CI..............: 1.232
T-value...................: 2.175832
P-value...................: 0.03018994
R^2.......................: 0.12124
Adjusted r^2..............: 0.081508
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + DiabetesStatus + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year DiabetesStatusDiabetes Med.Statin.LLDyes
348.14977 0.08718 0.31526 -0.17399 0.20694 -0.24560
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.82919 -0.56626 -0.07603 0.44307 2.90328
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 358.629055 99.358952 3.609 0.000354 ***
currentDF[, TRAIT] 0.073564 0.054208 1.357 0.175673
Age -0.001779 0.006266 -0.284 0.776669
Gendermale 0.326305 0.110626 2.950 0.003405 **
ORdate_year -0.178688 0.049575 -3.604 0.000360 ***
Hypertension.compositeyes -0.147400 0.150938 -0.977 0.329491
DiabetesStatusDiabetes 0.222332 0.128788 1.726 0.085205 .
SmokerStatusEx-smoker -0.060232 0.111996 -0.538 0.591069
SmokerStatusNever smoked 0.074090 0.168858 0.439 0.661109
Med.Statin.LLDyes -0.260920 0.115344 -2.262 0.024330 *
Med.all.antiplateletyes -0.214591 0.187904 -1.142 0.254257
GFR_MDRD -0.003189 0.002781 -1.147 0.252316
BMI -0.018247 0.013473 -1.354 0.176535
MedHx_CVDyes 0.109407 0.105124 1.041 0.298741
stenose50-70% -0.265247 0.714856 -0.371 0.710834
stenose70-90% 0.116664 0.666864 0.175 0.861229
stenose90-99% 0.032181 0.664416 0.048 0.961399
stenose100% (Occlusion) -0.846591 0.824170 -1.027 0.305063
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.921 on 336 degrees of freedom
Multiple R-squared: 0.1307, Adjusted R-squared: 0.08671
F-statistic: 2.972 on 17 and 336 DF, p-value: 8.305e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.073564
Standard error............: 0.054208
Odds ratio (effect size)..: 1.076
Lower 95% CI..............: 0.968
Upper 95% CI..............: 1.197
T-value...................: 1.357061
P-value...................: 0.1756727
R^2.......................: 0.130697
Adjusted r^2..............: 0.086715
Sample size of AE DB......: 2423
Sample size of model......: 354
Missing data %............: 85.39001
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
387.557181 0.176005 0.228372 -0.193386 -0.155389 -0.255899
GFR_MDRD
-0.003847
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.60973 -0.58187 -0.07439 0.49164 2.76577
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 398.165295 89.034156 4.472 1.03e-05 ***
currentDF[, TRAIT] 0.171332 0.045934 3.730 0.000221 ***
Age 0.002770 0.005859 0.473 0.636586
Gendermale 0.249260 0.101988 2.444 0.014985 *
ORdate_year -0.198710 0.044423 -4.473 1.02e-05 ***
Hypertension.compositeyes -0.007183 0.138807 -0.052 0.958757
DiabetesStatusDiabetes 0.140120 0.116610 1.202 0.230274
SmokerStatusEx-smoker -0.117795 0.103470 -1.138 0.255664
SmokerStatusNever smoked 0.024242 0.159003 0.152 0.878904
Med.Statin.LLDyes -0.179756 0.107285 -1.676 0.094669 .
Med.all.antiplateletyes -0.250028 0.163436 -1.530 0.126904
GFR_MDRD -0.002801 0.002612 -1.073 0.284179
BMI -0.013525 0.012627 -1.071 0.284821
MedHx_CVDyes 0.078918 0.096497 0.818 0.413974
stenose50-70% -0.242528 0.689611 -0.352 0.725270
stenose70-90% 0.247590 0.648124 0.382 0.702671
stenose90-99% 0.103883 0.645828 0.161 0.872296
stenose100% (Occlusion) -0.563330 0.802636 -0.702 0.483208
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8992 on 375 degrees of freedom
Multiple R-squared: 0.142, Adjusted R-squared: 0.1031
F-statistic: 3.652 on 17 and 375 DF, p-value: 1.799e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.171332
Standard error............: 0.045934
Odds ratio (effect size)..: 1.187
Lower 95% CI..............: 1.085
Upper 95% CI..............: 1.299
T-value...................: 3.729993
P-value...................: 0.0002210852
R^2.......................: 0.142042
Adjusted r^2..............: 0.103148
Sample size of AE DB......: 2423
Sample size of model......: 393
Missing data %............: 83.78044
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
330.96847 0.13643 0.26079 -0.16535 -0.16932 -0.23566
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.30981 0.21930 0.08342 -0.66366
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.77943 -0.59964 -0.04075 0.47551 2.95561
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 339.289933 91.849462 3.694 0.000254 ***
currentDF[, TRAIT] 0.138783 0.047865 2.899 0.003957 **
Age 0.003679 0.005926 0.621 0.535045
Gendermale 0.273430 0.101774 2.687 0.007538 **
ORdate_year -0.169320 0.045823 -3.695 0.000252 ***
Hypertension.compositeyes -0.047009 0.139031 -0.338 0.735460
DiabetesStatusDiabetes 0.133514 0.117326 1.138 0.255855
SmokerStatusEx-smoker -0.084940 0.103684 -0.819 0.413184
SmokerStatusNever smoked 0.037356 0.159790 0.234 0.815280
Med.Statin.LLDyes -0.175236 0.107771 -1.626 0.104786
Med.all.antiplateletyes -0.204007 0.163981 -1.244 0.214242
GFR_MDRD -0.002214 0.002635 -0.840 0.401198
BMI -0.016811 0.012687 -1.325 0.185943
MedHx_CVDyes 0.057699 0.097157 0.594 0.552957
stenose50-70% -0.334540 0.694012 -0.482 0.630060
stenose70-90% 0.188147 0.652329 0.288 0.773182
stenose90-99% 0.039120 0.650224 0.060 0.952057
stenose100% (Occlusion) -0.709359 0.805817 -0.880 0.379260
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9045 on 376 degrees of freedom
Multiple R-squared: 0.1296, Adjusted R-squared: 0.09028
F-statistic: 3.294 on 17 and 376 DF, p-value: 1.314e-05
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.138783
Standard error............: 0.047865
Odds ratio (effect size)..: 1.149
Lower 95% CI..............: 1.046
Upper 95% CI..............: 1.262
T-value...................: 2.899477
P-value...................: 0.003957249
R^2.......................: 0.129635
Adjusted r^2..............: 0.090284
Sample size of AE DB......: 2423
Sample size of model......: 394
Missing data %............: 83.73917
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.all.antiplatelet + GFR_MDRD + BMI + stenose,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.all.antiplateletyes GFR_MDRD
605.44365 0.21623 0.27216 -0.30240 -0.27935 -0.00511
BMI stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.02159 0.80800 1.23457 1.03777 0.19344
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.6355 -0.5328 -0.1320 0.4369 2.7713
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 603.371833 102.046952 5.913 8.83e-09 ***
currentDF[, TRAIT] 0.204483 0.049258 4.151 4.27e-05 ***
Age 0.001610 0.006012 0.268 0.7890
Gendermale 0.298705 0.104647 2.854 0.0046 **
ORdate_year -0.301318 0.050931 -5.916 8.66e-09 ***
Hypertension.compositeyes -0.163890 0.145500 -1.126 0.2609
DiabetesStatusDiabetes -0.013334 0.117617 -0.113 0.9098
SmokerStatusEx-smoker -0.087816 0.105082 -0.836 0.4040
SmokerStatusNever smoked 0.158093 0.167713 0.943 0.3466
Med.Statin.LLDyes -0.072908 0.108889 -0.670 0.5036
Med.all.antiplateletyes -0.263286 0.158782 -1.658 0.0983 .
GFR_MDRD -0.005346 0.002544 -2.102 0.0364 *
BMI -0.020340 0.013165 -1.545 0.1233
MedHx_CVDyes 0.050893 0.099678 0.511 0.6100
stenose50-70% 0.735314 0.889422 0.827 0.4090
stenose70-90% 1.178954 0.852140 1.384 0.1675
stenose90-99% 0.965463 0.850819 1.135 0.2574
stenose100% (Occlusion) 0.087200 0.959619 0.091 0.9277
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8426 on 312 degrees of freedom
Multiple R-squared: 0.2061, Adjusted R-squared: 0.1628
F-statistic: 4.764 on 17 and 312 DF, p-value: 4.949e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.204483
Standard error............: 0.049258
Odds ratio (effect size)..: 1.227
Lower 95% CI..............: 1.114
Upper 95% CI..............: 1.351
T-value...................: 4.151285
P-value...................: 4.270246e-05
R^2.......................: 0.206079
Adjusted r^2..............: 0.162821
Sample size of AE DB......: 2423
Sample size of model......: 330
Missing data %............: 86.38052
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year GFR_MDRD
422.567714 0.245472 -0.211023 -0.004055
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.75331 -0.60293 -0.06849 0.46663 3.03503
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 397.931460 94.466998 4.212 3.19e-05 ***
currentDF[, TRAIT] 0.060049 0.048611 1.235 0.2175
Age 0.000681 0.006075 0.112 0.9108
Gendermale 0.271881 0.105766 2.571 0.0105 *
ORdate_year -0.198487 0.047134 -4.211 3.20e-05 ***
Hypertension.compositeyes -0.074295 0.142203 -0.522 0.6017
DiabetesStatusDiabetes 0.108392 0.119629 0.906 0.3655
SmokerStatusEx-smoker -0.077703 0.106553 -0.729 0.4663
SmokerStatusNever smoked 0.143648 0.165490 0.868 0.3860
Med.Statin.LLDyes -0.159324 0.110967 -1.436 0.1519
Med.all.antiplateletyes -0.189435 0.169337 -1.119 0.2640
GFR_MDRD -0.003119 0.002710 -1.151 0.2506
BMI -0.015967 0.013179 -1.212 0.2265
MedHx_CVDyes 0.077823 0.099817 0.780 0.4361
stenose50-70% -0.325158 0.703195 -0.462 0.6441
stenose70-90% 0.216771 0.663164 0.327 0.7439
stenose90-99% 0.091778 0.660753 0.139 0.8896
stenose100% (Occlusion) -0.813254 0.819977 -0.992 0.3219
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.919 on 366 degrees of freedom
Multiple R-squared: 0.1153, Adjusted R-squared: 0.07423
F-statistic: 2.806 on 17 and 366 DF, p-value: 0.000188
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.060049
Standard error............: 0.048611
Odds ratio (effect size)..: 1.062
Lower 95% CI..............: 0.965
Upper 95% CI..............: 1.168
T-value...................: 1.235294
P-value...................: 0.2175134
R^2.......................: 0.11532
Adjusted r^2..............: 0.074228
Sample size of AE DB......: 2423
Sample size of model......: 384
Missing data %............: 84.15188
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes GFR_MDRD
488.244559 0.075016 0.316277 -0.243790 -0.160502 -0.003945
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.8689 -0.5762 -0.0745 0.4982 3.2666
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.951e+02 9.547e+01 5.186 3.54e-07 ***
currentDF[, TRAIT] 6.707e-02 5.122e-02 1.309 0.19125
Age -8.326e-04 5.900e-03 -0.141 0.88785
Gendermale 3.346e-01 1.030e-01 3.250 0.00126 **
ORdate_year -2.469e-01 4.763e-02 -5.185 3.57e-07 ***
Hypertension.compositeyes -4.015e-02 1.437e-01 -0.279 0.78010
DiabetesStatusDiabetes 5.294e-02 1.166e-01 0.454 0.65017
SmokerStatusEx-smoker -8.013e-02 1.043e-01 -0.768 0.44288
SmokerStatusNever smoked 1.326e-01 1.611e-01 0.823 0.41093
Med.Statin.LLDyes -1.705e-01 1.071e-01 -1.592 0.11228
Med.all.antiplateletyes -2.210e-01 1.689e-01 -1.309 0.19140
GFR_MDRD -3.499e-03 2.617e-03 -1.337 0.18207
BMI -1.624e-02 1.301e-02 -1.248 0.21285
MedHx_CVDyes 8.124e-02 9.803e-02 0.829 0.40776
stenose50-70% -2.629e-01 6.925e-01 -0.380 0.70438
stenose70-90% 1.794e-01 6.541e-01 0.274 0.78401
stenose90-99% 6.799e-02 6.515e-01 0.104 0.91695
stenose100% (Occlusion) -8.184e-01 8.078e-01 -1.013 0.31168
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9055 on 371 degrees of freedom
Multiple R-squared: 0.1412, Adjusted R-squared: 0.1018
F-statistic: 3.588 on 17 and 371 DF, p-value: 2.607e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.067066
Standard error............: 0.051224
Odds ratio (effect size)..: 1.069
Lower 95% CI..............: 0.967
Upper 95% CI..............: 1.182
T-value...................: 1.309275
P-value...................: 0.1912516
R^2.......................: 0.141198
Adjusted r^2..............: 0.101845
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + BMI + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes BMI
518.11414 0.22361 0.20944 -0.25837 -0.16145 -0.02197
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.68467 -0.21566 -0.27056 -1.16796
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.06404 -0.54875 -0.05214 0.45277 3.05284
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 528.162165 92.488230 5.711 2.31e-08 ***
currentDF[, TRAIT] 0.217754 0.048550 4.485 9.73e-06 ***
Age -0.001146 0.005728 -0.200 0.8415
Gendermale 0.232349 0.102014 2.278 0.0233 *
ORdate_year -0.263143 0.046144 -5.703 2.41e-08 ***
Hypertension.compositeyes -0.101608 0.138007 -0.736 0.4620
DiabetesStatusDiabetes 0.101299 0.114406 0.885 0.3765
SmokerStatusEx-smoker -0.039920 0.101825 -0.392 0.6952
SmokerStatusNever smoked 0.189772 0.156136 1.215 0.2250
Med.Statin.LLDyes -0.156760 0.104590 -1.499 0.1348
Med.all.antiplateletyes -0.155969 0.164814 -0.946 0.3446
GFR_MDRD -0.002493 0.002562 -0.973 0.3313
BMI -0.024847 0.012805 -1.940 0.0531 .
MedHx_CVDyes 0.076311 0.095565 0.799 0.4251
stenose50-70% -0.663006 0.680455 -0.974 0.3305
stenose70-90% -0.237089 0.644417 -0.368 0.7131
stenose90-99% -0.290772 0.640210 -0.454 0.6500
stenose100% (Occlusion) -1.290942 0.795104 -1.624 0.1053
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8839 on 371 degrees of freedom
Multiple R-squared: 0.1816, Adjusted R-squared: 0.1441
F-statistic: 4.843 on 17 and 371 DF, p-value: 2.095e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.217754
Standard error............: 0.04855
Odds ratio (effect size)..: 1.243
Lower 95% CI..............: 1.13
Upper 95% CI..............: 1.367
T-value...................: 4.485114
P-value...................: 9.730762e-06
R^2.......................: 0.181604
Adjusted r^2..............: 0.144104
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD + Med.all.antiplatelet + BMI +
stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
484.54051 0.12798 0.25522 -0.24170 -0.16630 -0.23587
BMI stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.01882 -0.37517 0.08306 -0.01948 -0.95485
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.92719 -0.56131 -0.06458 0.43887 3.07875
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 485.689729 94.610884 5.134 4.6e-07 ***
currentDF[, TRAIT] 0.119163 0.046927 2.539 0.01151 *
Age -0.001413 0.005830 -0.242 0.80859
Gendermale 0.287381 0.102738 2.797 0.00542 **
ORdate_year -0.242140 0.047207 -5.129 4.7e-07 ***
Hypertension.compositeyes -0.049814 0.140702 -0.354 0.72351
DiabetesStatusDiabetes 0.074169 0.116263 0.638 0.52391
SmokerStatusEx-smoker -0.060501 0.103495 -0.585 0.55919
SmokerStatusNever smoked 0.147501 0.158853 0.929 0.35373
Med.Statin.LLDyes -0.174343 0.106397 -1.639 0.10214
Med.all.antiplateletyes -0.207719 0.167400 -1.241 0.21544
GFR_MDRD -0.002866 0.002617 -1.095 0.27418
BMI -0.020951 0.013005 -1.611 0.10803
MedHx_CVDyes 0.088529 0.097239 0.910 0.36319
stenose50-70% -0.322255 0.687739 -0.469 0.63965
stenose70-90% 0.113538 0.650027 0.175 0.86144
stenose90-99% -0.002686 0.647492 -0.004 0.99669
stenose100% (Occlusion) -0.905485 0.803171 -1.127 0.26031
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8998 on 371 degrees of freedom
Multiple R-squared: 0.152, Adjusted R-squared: 0.1131
F-statistic: 3.911 on 17 and 371 DF, p-value: 4.25e-07
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_pg_ml_2015_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_pg_ml_2015_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.119163
Standard error............: 0.046927
Odds ratio (effect size)..: 1.127
Lower 95% CI..............: 1.028
Upper 95% CI..............: 1.235
T-value...................: 2.539309
P-value...................: 0.01151472
R^2.......................: 0.151969
Adjusted r^2..............: 0.11311
Sample size of AE DB......: 2423
Sample size of model......: 389
Missing data %............: 83.94552
Analysis of MCP1_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
337.77686 -0.07302 0.22973 -0.16864
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3564 -0.6994 -0.0096 0.6442 2.3502
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 324.135124 114.029542 2.843 0.00473 **
currentDF[, TRAIT] -0.091842 0.052701 -1.743 0.08225 .
Age -0.010907 0.006841 -1.594 0.11173
Gendermale 0.280439 0.121542 2.307 0.02161 *
ORdate_year -0.161011 0.056903 -2.830 0.00493 **
Hypertension.compositeyes -0.222927 0.155898 -1.430 0.15361
DiabetesStatusDiabetes -0.112047 0.138121 -0.811 0.41778
SmokerStatusEx-smoker 0.057259 0.119725 0.478 0.63276
SmokerStatusNever smoked 0.369203 0.179991 2.051 0.04098 *
Med.Statin.LLDyes -0.168555 0.119333 -1.412 0.15869
Med.all.antiplateletyes 0.234563 0.205958 1.139 0.25552
GFR_MDRD -0.002512 0.003084 -0.815 0.41586
BMI -0.007833 0.015379 -0.509 0.61084
MedHx_CVDyes 0.042501 0.110771 0.384 0.70144
stenose50-70% -0.622075 0.779274 -0.798 0.42525
stenose70-90% -0.509955 0.717059 -0.711 0.47744
stenose90-99% -0.556706 0.715558 -0.778 0.43709
stenose100% (Occlusion) -0.781108 0.887784 -0.880 0.37954
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9891 on 354 degrees of freedom
Multiple R-squared: 0.06962, Adjusted R-squared: 0.02494
F-statistic: 1.558 on 17 and 354 DF, p-value: 0.073
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.091842
Standard error............: 0.052701
Odds ratio (effect size)..: 0.912
Lower 95% CI..............: 0.823
Upper 95% CI..............: 1.012
T-value...................: -1.742702
P-value...................: 0.08225396
R^2.......................: 0.069619
Adjusted r^2..............: 0.02494
Sample size of AE DB......: 2423
Sample size of model......: 372
Missing data %............: 84.64713
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
391.3396 0.3126 -0.1954
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3067 -0.7046 0.0354 0.6417 2.5773
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 366.184851 124.633598 2.938 0.00354 **
currentDF[, TRAIT] -0.050504 0.056698 -0.891 0.37371
Age -0.013894 0.007279 -1.909 0.05715 .
Gendermale 0.354830 0.129438 2.741 0.00646 **
ORdate_year -0.182242 0.062216 -2.929 0.00364 **
Hypertension.compositeyes -0.112361 0.165304 -0.680 0.49716
DiabetesStatusDiabetes -0.118886 0.145318 -0.818 0.41389
SmokerStatusEx-smoker 0.089026 0.124760 0.714 0.47600
SmokerStatusNever smoked 0.341531 0.190332 1.794 0.07368 .
Med.Statin.LLDyes -0.156028 0.126548 -1.233 0.21848
Med.all.antiplateletyes 0.158071 0.219711 0.719 0.47238
GFR_MDRD -0.003372 0.003474 -0.971 0.33234
BMI -0.003079 0.016501 -0.187 0.85209
MedHx_CVDyes 0.061412 0.117000 0.525 0.60002
stenose50-70% -0.093615 1.077451 -0.087 0.93082
stenose70-90% -0.040576 1.030674 -0.039 0.96862
stenose90-99% -0.058983 1.027499 -0.057 0.95426
stenose100% (Occlusion) -0.257200 1.161929 -0.221 0.82495
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.001 on 325 degrees of freedom
Multiple R-squared: 0.07236, Adjusted R-squared: 0.02384
F-statistic: 1.491 on 17 and 325 DF, p-value: 0.09541
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.050504
Standard error............: 0.056698
Odds ratio (effect size)..: 0.951
Lower 95% CI..............: 0.851
Upper 95% CI..............: 1.062
T-value...................: -0.89077
P-value...................: 0.3737117
R^2.......................: 0.072361
Adjusted r^2..............: 0.023838
Sample size of AE DB......: 2423
Sample size of model......: 343
Missing data %............: 85.844
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Med.Statin.LLDyes
348.44629 -0.11317 -0.01065 0.29540 -0.17354 -0.21941
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5069 -0.7021 0.0220 0.6314 2.2770
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 346.815626 117.537234 2.951 0.00339 **
currentDF[, TRAIT] -0.116354 0.053150 -2.189 0.02925 *
Age -0.015717 0.006997 -2.246 0.02532 *
Gendermale 0.344607 0.121863 2.828 0.00496 **
ORdate_year -0.172428 0.058668 -2.939 0.00351 **
Hypertension.compositeyes -0.111181 0.155967 -0.713 0.47642
DiabetesStatusDiabetes -0.141743 0.140027 -1.012 0.31213
SmokerStatusEx-smoker 0.073269 0.119671 0.612 0.54077
SmokerStatusNever smoked 0.313783 0.185013 1.696 0.09078 .
Med.Statin.LLDyes -0.235154 0.120323 -1.954 0.05146 .
Med.all.antiplateletyes 0.160528 0.211803 0.758 0.44902
GFR_MDRD -0.003817 0.003191 -1.196 0.23245
BMI -0.004306 0.015178 -0.284 0.77683
MedHx_CVDyes 0.092628 0.111450 0.831 0.40648
stenose50-70% -0.046230 1.060366 -0.044 0.96525
stenose70-90% -0.051028 1.012527 -0.050 0.95983
stenose90-99% -0.077836 1.010546 -0.077 0.93865
stenose100% (Occlusion) -0.339590 1.141656 -0.297 0.76630
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9872 on 346 degrees of freedom
Multiple R-squared: 0.08442, Adjusted R-squared: 0.03944
F-statistic: 1.877 on 17 and 346 DF, p-value: 0.01904
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.116354
Standard error............: 0.05315
Odds ratio (effect size)..: 0.89
Lower 95% CI..............: 0.802
Upper 95% CI..............: 0.988
T-value...................: -2.189154
P-value...................: 0.02925333
R^2.......................: 0.084423
Adjusted r^2..............: 0.039438
Sample size of AE DB......: 2423
Sample size of model......: 364
Missing data %............: 84.9773
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
303.5723 0.2429 -0.1516
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1707 -0.6816 -0.0241 0.6683 2.4450
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 292.598955 112.086171 2.610 0.00942 **
currentDF[, TRAIT] 0.051411 0.051375 1.001 0.31764
Age -0.014239 0.007037 -2.023 0.04375 *
Gendermale 0.310939 0.121720 2.555 0.01104 *
ORdate_year -0.145267 0.055919 -2.598 0.00976 **
Hypertension.compositeyes -0.143512 0.156731 -0.916 0.36045
DiabetesStatusDiabetes -0.081443 0.137404 -0.593 0.55373
SmokerStatusEx-smoker 0.098193 0.118217 0.831 0.40673
SmokerStatusNever smoked 0.387337 0.180654 2.144 0.03269 *
Med.Statin.LLDyes -0.156889 0.118637 -1.322 0.18685
Med.all.antiplateletyes 0.157364 0.191973 0.820 0.41291
GFR_MDRD -0.003912 0.003160 -1.238 0.21647
BMI -0.012374 0.014417 -0.858 0.39130
MedHx_CVDyes 0.029785 0.109981 0.271 0.78669
stenose50-70% -0.238627 0.655442 -0.364 0.71602
stenose70-90% -0.088126 0.597638 -0.147 0.88285
stenose90-99% -0.162556 0.595502 -0.273 0.78503
stenose100% (Occlusion) -0.411221 0.797116 -0.516 0.60625
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.001 on 364 degrees of freedom
Multiple R-squared: 0.06287, Adjusted R-squared: 0.01911
F-statistic: 1.437 on 17 and 364 DF, p-value: 0.1162
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL6_rank
Effect size...............: 0.051411
Standard error............: 0.051375
Odds ratio (effect size)..: 1.053
Lower 95% CI..............: 0.952
Upper 95% CI..............: 1.164
T-value...................: 1.00069
P-value...................: 0.3176413
R^2.......................: 0.062873
Adjusted r^2..............: 0.019106
Sample size of AE DB......: 2423
Sample size of model......: 382
Missing data %............: 84.23442
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + SmokerStatus +
Med.all.antiplatelet + GFR_MDRD + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
483.713537 0.324434 -0.018361 0.307909 -0.240502
Hypertension.compositeyes SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes GFR_MDRD
-0.266232 0.103620 0.407957 0.324185 -0.004823
BMI
-0.020470
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2578 -0.6013 -0.0235 0.5901 2.3891
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 468.385552 104.233897 4.494 9.53e-06 ***
currentDF[, TRAIT] 0.323207 0.051592 6.265 1.10e-09 ***
Age -0.018659 0.006544 -2.851 0.00461 **
Gendermale 0.316040 0.113877 2.775 0.00581 **
ORdate_year -0.232817 0.052003 -4.477 1.03e-05 ***
Hypertension.compositeyes -0.225756 0.148680 -1.518 0.12981
DiabetesStatusDiabetes -0.160255 0.129844 -1.234 0.21795
SmokerStatusEx-smoker 0.106100 0.110630 0.959 0.33819
SmokerStatusNever smoked 0.427245 0.175653 2.432 0.01550 *
Med.Statin.LLDyes -0.099350 0.110753 -0.897 0.37032
Med.all.antiplateletyes 0.292253 0.175618 1.664 0.09698 .
GFR_MDRD -0.004916 0.002823 -1.741 0.08249 .
BMI -0.018124 0.013438 -1.349 0.17831
MedHx_CVDyes 0.024877 0.104174 0.239 0.81140
stenose50-70% -0.056165 0.616116 -0.091 0.92742
stenose70-90% 0.029985 0.554347 0.054 0.95689
stenose90-99% -0.066973 0.553139 -0.121 0.90370
stenose100% (Occlusion) -0.730295 0.726805 -1.005 0.31569
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9299 on 350 degrees of freedom
Multiple R-squared: 0.192, Adjusted R-squared: 0.1528
F-statistic: 4.893 on 17 and 350 DF, p-value: 1.81e-09
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL8_rank
Effect size...............: 0.323207
Standard error............: 0.051592
Odds ratio (effect size)..: 1.382
Lower 95% CI..............: 1.249
Upper 95% CI..............: 1.529
T-value...................: 6.264673
P-value...................: 1.09668e-09
R^2.......................: 0.192008
Adjusted r^2..............: 0.152763
Sample size of AE DB......: 2423
Sample size of model......: 368
Missing data %............: 84.81222
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes
469.1433 0.2651 0.2845 -0.2341 -0.1561
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.97853 -0.64237 -0.04494 0.58360 2.41010
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 450.887553 78.695200 5.730 1.90e-08 ***
currentDF[, TRAIT] 0.256934 0.042621 6.028 3.57e-09 ***
Age -0.008089 0.005645 -1.433 0.15261
Gendermale 0.306162 0.097611 3.137 0.00183 **
ORdate_year -0.224472 0.039276 -5.715 2.05e-08 ***
Hypertension.compositeyes -0.142938 0.130384 -1.096 0.27357
DiabetesStatusDiabetes -0.116201 0.111687 -1.040 0.29873
SmokerStatusEx-smoker 0.036425 0.097594 0.373 0.70916
SmokerStatusNever smoked 0.143082 0.141142 1.014 0.31128
Med.Statin.LLDyes -0.167205 0.101227 -1.652 0.09931 .
Med.all.antiplateletyes 0.053742 0.158187 0.340 0.73422
GFR_MDRD -0.001307 0.002394 -0.546 0.58537
BMI -0.009911 0.011498 -0.862 0.38918
MedHx_CVDyes 0.040135 0.091466 0.439 0.66103
stenose50-70% -0.392710 0.574523 -0.684 0.49463
stenose70-90% -0.173669 0.530032 -0.328 0.74333
stenose90-99% -0.172148 0.528067 -0.326 0.74459
stenose100% (Occlusion) -0.930035 0.670103 -1.388 0.16589
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8952 on 430 degrees of freedom
Multiple R-squared: 0.1963, Adjusted R-squared: 0.1645
F-statistic: 6.178 on 17 and 430 DF, p-value: 5.855e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.256934
Standard error............: 0.042621
Odds ratio (effect size)..: 1.293
Lower 95% CI..............: 1.189
Upper 95% CI..............: 1.406
T-value...................: 6.028403
P-value...................: 3.566497e-09
R^2.......................: 0.196293
Adjusted r^2..............: 0.164519
Sample size of AE DB......: 2423
Sample size of model......: 448
Missing data %............: 81.51052
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Med.Statin.LLDyes
377.35088 -0.13938 -0.01222 0.31984 -0.18793 -0.25406
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4252 -0.7168 -0.0178 0.6097 2.3442
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 382.837732 133.005231 2.878 0.00428 **
currentDF[, TRAIT] -0.148079 0.059120 -2.505 0.01277 *
Age -0.018197 0.007506 -2.424 0.01591 *
Gendermale 0.381495 0.132668 2.876 0.00432 **
ORdate_year -0.190096 0.066363 -2.864 0.00446 **
Hypertension.compositeyes -0.163857 0.174140 -0.941 0.34747
DiabetesStatusDiabetes -0.056989 0.150146 -0.380 0.70454
SmokerStatusEx-smoker 0.062815 0.129141 0.486 0.62702
SmokerStatusNever smoked 0.343998 0.194722 1.767 0.07829 .
Med.Statin.LLDyes -0.270007 0.129064 -2.092 0.03726 *
Med.all.antiplateletyes 0.085201 0.235966 0.361 0.71829
GFR_MDRD -0.004142 0.003542 -1.169 0.24312
BMI -0.011549 0.016701 -0.692 0.48974
MedHx_CVDyes 0.045507 0.122897 0.370 0.71142
stenose50-70% -0.349383 1.088293 -0.321 0.74840
stenose70-90% -0.193352 1.034790 -0.187 0.85190
stenose90-99% -0.199793 1.032638 -0.193 0.84671
stenose100% (Occlusion) -0.839284 1.225427 -0.685 0.49393
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 1.003 on 307 degrees of freedom
Multiple R-squared: 0.09275, Adjusted R-squared: 0.04251
F-statistic: 1.846 on 17 and 307 DF, p-value: 0.02231
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.148079
Standard error............: 0.05912
Odds ratio (effect size)..: 0.862
Lower 95% CI..............: 0.768
Upper 95% CI..............: 0.968
T-value...................: -2.504723
P-value...................: 0.01277281
R^2.......................: 0.09275
Adjusted r^2..............: 0.042511
Sample size of AE DB......: 2423
Sample size of model......: 325
Missing data %............: 86.58688
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Med.Statin.LLDyes
369.441292 -0.125855 -0.009407 0.319861 -0.184087 -0.206542
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5441 -0.6935 -0.0074 0.6107 2.4391
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 365.217756 125.908830 2.901 0.00398 **
currentDF[, TRAIT] -0.129343 0.056270 -2.299 0.02216 *
Age -0.014677 0.007287 -2.014 0.04483 *
Gendermale 0.380569 0.126539 3.008 0.00284 **
ORdate_year -0.181562 0.062824 -2.890 0.00411 **
Hypertension.compositeyes -0.113279 0.165456 -0.685 0.49405
DiabetesStatusDiabetes -0.050993 0.142719 -0.357 0.72110
SmokerStatusEx-smoker 0.029611 0.126046 0.235 0.81441
SmokerStatusNever smoked 0.325595 0.186266 1.748 0.08141 .
Med.Statin.LLDyes -0.217762 0.126329 -1.724 0.08570 .
Med.all.antiplateletyes 0.175223 0.214845 0.816 0.41534
GFR_MDRD -0.004207 0.003391 -1.241 0.21561
BMI -0.009384 0.016360 -0.574 0.56665
MedHx_CVDyes 0.065882 0.116576 0.565 0.57237
stenose50-70% -0.180538 1.066122 -0.169 0.86563
stenose70-90% -0.099681 1.019576 -0.098 0.92218
stenose90-99% -0.140547 1.016673 -0.138 0.89013
stenose100% (Occlusion) -0.503419 1.179534 -0.427 0.66981
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9904 on 325 degrees of freedom
Multiple R-squared: 0.08593, Adjusted R-squared: 0.03812
F-statistic: 1.797 on 17 and 325 DF, p-value: 0.02731
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.129343
Standard error............: 0.05627
Odds ratio (effect size)..: 0.879
Lower 95% CI..............: 0.787
Upper 95% CI..............: 0.981
T-value...................: -2.298604
P-value...................: 0.02216168
R^2.......................: 0.085935
Adjusted r^2..............: 0.038122
Sample size of AE DB......: 2423
Sample size of model......: 343
Missing data %............: 85.844
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
481.9463 0.3897 0.2204 -0.2405 -0.1661
Med.Statin.LLDyes
-0.1353
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.6353 -0.6508 -0.0352 0.6057 2.4524
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.810e+02 7.720e+01 6.230 1.02e-09 ***
currentDF[, TRAIT] 3.825e-01 4.102e-02 9.324 < 2e-16 ***
Age -2.655e-03 5.378e-03 -0.494 0.6217
Gendermale 2.295e-01 9.296e-02 2.468 0.0139 *
ORdate_year -2.396e-01 3.853e-02 -6.219 1.09e-09 ***
Hypertension.compositeyes -1.494e-01 1.220e-01 -1.224 0.2214
DiabetesStatusDiabetes -6.109e-02 1.038e-01 -0.589 0.5563
SmokerStatusEx-smoker 1.574e-02 9.221e-02 0.171 0.8645
SmokerStatusNever smoked 1.052e-01 1.373e-01 0.766 0.4440
Med.Statin.LLDyes -1.519e-01 9.556e-02 -1.589 0.1127
Med.all.antiplateletyes 3.047e-02 1.470e-01 0.207 0.8358
GFR_MDRD 4.795e-04 2.297e-03 0.209 0.8347
BMI -1.179e-02 1.097e-02 -1.074 0.2832
MedHx_CVDyes 2.197e-02 8.629e-02 0.255 0.7991
stenose50-70% -4.282e-01 5.707e-01 -0.750 0.4535
stenose70-90% -2.634e-01 5.303e-01 -0.497 0.6197
stenose90-99% -3.147e-01 5.287e-01 -0.595 0.5520
stenose100% (Occlusion) -9.036e-01 6.705e-01 -1.348 0.1784
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8978 on 479 degrees of freedom
Multiple R-squared: 0.2369, Adjusted R-squared: 0.2098
F-statistic: 8.748 on 17 and 479 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.382482
Standard error............: 0.04102
Odds ratio (effect size)..: 1.466
Lower 95% CI..............: 1.353
Upper 95% CI..............: 1.589
T-value...................: 9.324199
P-value...................: 4.12287e-19
R^2.......................: 0.236913
Adjusted r^2..............: 0.20983
Sample size of AE DB......: 2423
Sample size of model......: 497
Missing data %............: 79.48824
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
471.7857 0.3464 0.2211 -0.2354 -0.2092
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5725 -0.6842 -0.0106 0.6285 2.2829
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.536e+02 7.884e+01 5.753 1.56e-08 ***
currentDF[, TRAIT] 3.404e-01 4.205e-02 8.095 4.74e-15 ***
Age -4.645e-03 5.483e-03 -0.847 0.3973
Gendermale 2.308e-01 9.504e-02 2.428 0.0155 *
ORdate_year -2.258e-01 3.934e-02 -5.740 1.68e-08 ***
Hypertension.compositeyes -1.792e-01 1.247e-01 -1.437 0.1514
DiabetesStatusDiabetes -7.635e-02 1.061e-01 -0.719 0.4723
SmokerStatusEx-smoker 2.884e-02 9.419e-02 0.306 0.7596
SmokerStatusNever smoked 1.377e-01 1.403e-01 0.982 0.3268
Med.Statin.LLDyes -1.409e-01 9.767e-02 -1.443 0.1498
Med.all.antiplateletyes 2.802e-02 1.505e-01 0.186 0.8524
GFR_MDRD 3.006e-04 2.350e-03 0.128 0.8983
BMI -1.342e-02 1.123e-02 -1.195 0.2326
MedHx_CVDyes 3.006e-02 8.811e-02 0.341 0.7332
stenose50-70% -4.718e-01 5.840e-01 -0.808 0.4196
stenose70-90% -2.817e-01 5.427e-01 -0.519 0.6039
stenose90-99% -3.361e-01 5.411e-01 -0.621 0.5348
stenose100% (Occlusion) -1.029e+00 6.860e-01 -1.500 0.1342
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9188 on 480 degrees of freedom
Multiple R-squared: 0.2063, Adjusted R-squared: 0.1782
F-statistic: 7.34 on 17 and 480 DF, p-value: 3.891e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.340369
Standard error............: 0.042046
Odds ratio (effect size)..: 1.405
Lower 95% CI..............: 1.294
Upper 95% CI..............: 1.526
T-value...................: 8.095211
P-value...................: 4.740966e-15
R^2.......................: 0.206317
Adjusted r^2..............: 0.178208
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + ORdate_year, data = currentDF)
Coefficients:
(Intercept) Gendermale ORdate_year
430.2780 0.3651 -0.2149
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3229 -0.6695 0.0116 0.6628 2.7270
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 483.732484 119.272545 4.056 6.17e-05 ***
currentDF[, TRAIT] -0.108540 0.057053 -1.902 0.05794 .
Age -0.012458 0.006953 -1.792 0.07403 .
Gendermale 0.369763 0.122970 3.007 0.00283 **
ORdate_year -0.240763 0.059497 -4.047 6.40e-05 ***
Hypertension.compositeyes -0.021201 0.159718 -0.133 0.89448
DiabetesStatusDiabetes -0.039525 0.135510 -0.292 0.77071
SmokerStatusEx-smoker 0.145721 0.119575 1.219 0.22380
SmokerStatusNever smoked 0.359357 0.181365 1.981 0.04833 *
Med.Statin.LLDyes -0.189488 0.122301 -1.549 0.12220
Med.all.antiplateletyes 0.108570 0.194470 0.558 0.57701
GFR_MDRD -0.001517 0.003074 -0.493 0.62206
BMI -0.018579 0.014625 -1.270 0.20480
MedHx_CVDyes 0.071023 0.113512 0.626 0.53193
stenose50-70% -0.045968 0.668659 -0.069 0.94523
stenose70-90% -0.253970 0.593765 -0.428 0.66911
stenose90-99% -0.194770 0.591953 -0.329 0.74233
stenose100% (Occlusion) -0.605486 0.835638 -0.725 0.46920
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9935 on 349 degrees of freedom
Multiple R-squared: 0.09256, Adjusted R-squared: 0.04836
F-statistic: 2.094 on 17 and 349 DF, p-value: 0.006979
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.10854
Standard error............: 0.057053
Odds ratio (effect size)..: 0.897
Lower 95% CI..............: 0.802
Upper 95% CI..............: 1.003
T-value...................: -1.902428
P-value...................: 0.05793809
R^2.......................: 0.092565
Adjusted r^2..............: 0.048363
Sample size of AE DB......: 2423
Sample size of model......: 367
Missing data %............: 84.85349
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Age + Gender + ORdate_year +
Med.Statin.LLD + Med.all.antiplatelet, data = currentDF)
Coefficients:
(Intercept) Age Gendermale ORdate_year Med.Statin.LLDyes Med.all.antiplateletyes
269.319313 -0.009533 0.294462 -0.134292 -0.179835 0.348119
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2611 -0.6760 0.0113 0.6116 2.6236
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 275.964697 125.935591 2.191 0.02915 *
currentDF[, TRAIT] -0.002156 0.054993 -0.039 0.96876
Age -0.014792 0.007229 -2.046 0.04156 *
Gendermale 0.349140 0.127517 2.738 0.00653 **
ORdate_year -0.137038 0.062850 -2.180 0.02996 *
Hypertension.compositeyes -0.121535 0.166625 -0.729 0.46630
DiabetesStatusDiabetes -0.092884 0.144284 -0.644 0.52020
SmokerStatusEx-smoker 0.053903 0.126442 0.426 0.67017
SmokerStatusNever smoked 0.407871 0.188700 2.161 0.03140 *
Med.Statin.LLDyes -0.199705 0.125586 -1.590 0.11279
Med.all.antiplateletyes 0.285589 0.224909 1.270 0.20508
GFR_MDRD -0.003156 0.003388 -0.931 0.35238
BMI -0.014823 0.016144 -0.918 0.35921
MedHx_CVDyes 0.025319 0.119106 0.213 0.83179
stenose50-70% -0.197069 1.067862 -0.185 0.85370
stenose70-90% -0.069896 1.020871 -0.068 0.94546
stenose90-99% -0.130683 1.019824 -0.128 0.89812
stenose100% (Occlusion) -0.819097 1.273028 -0.643 0.52041
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9942 on 318 degrees of freedom
Multiple R-squared: 0.07925, Adjusted R-squared: 0.03003
F-statistic: 1.61 on 17 and 318 DF, p-value: 0.06002
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TNFA_rank
Effect size...............: -0.002156
Standard error............: 0.054993
Odds ratio (effect size)..: 0.998
Lower 95% CI..............: 0.896
Upper 95% CI..............: 1.111
T-value...................: -0.039197
P-value...................: 0.9687576
R^2.......................: 0.079251
Adjusted r^2..............: 0.030029
Sample size of AE DB......: 2423
Sample size of model......: 336
Missing data %............: 86.13289
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + SmokerStatus, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
164.54415 0.35958 0.25142 -0.08215 -0.20282
SmokerStatusEx-smoker SmokerStatusNever smoked
0.10913 0.27908
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.2591 -0.6027 -0.0388 0.6311 2.5878
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 146.080149 87.467299 1.670 0.0955 .
currentDF[, TRAIT] 0.359164 0.048534 7.400 6.13e-13 ***
Age -0.006303 0.005520 -1.142 0.2541
Gendermale 0.247108 0.095861 2.578 0.0102 *
ORdate_year -0.072430 0.043645 -1.660 0.0977 .
Hypertension.compositeyes -0.147063 0.126216 -1.165 0.2445
DiabetesStatusDiabetes -0.010037 0.107585 -0.093 0.9257
SmokerStatusEx-smoker 0.149550 0.095041 1.574 0.1163
SmokerStatusNever smoked 0.325111 0.140706 2.311 0.0213 *
Med.Statin.LLDyes -0.154385 0.098659 -1.565 0.1183
Med.all.antiplateletyes 0.070551 0.151685 0.465 0.6421
GFR_MDRD 0.001645 0.002388 0.689 0.4911
BMI -0.014946 0.011350 -1.317 0.1885
MedHx_CVDyes 0.020318 0.089007 0.228 0.8195
stenose50-70% -0.450650 0.589894 -0.764 0.4453
stenose70-90% -0.303103 0.548176 -0.553 0.5806
stenose90-99% -0.372707 0.546588 -0.682 0.4956
stenose100% (Occlusion) -0.896842 0.693084 -1.294 0.1963
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.928 on 480 degrees of freedom
Multiple R-squared: 0.1903, Adjusted R-squared: 0.1617
F-statistic: 6.637 on 17 and 480 DF, p-value: 2.58e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.359164
Standard error............: 0.048534
Odds ratio (effect size)..: 1.432
Lower 95% CI..............: 1.302
Upper 95% CI..............: 1.575
T-value...................: 7.400196
P-value...................: 6.131325e-13
R^2.......................: 0.190333
Adjusted r^2..............: 0.161658
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing MCP1_rankattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsenseattempting model selection on an essentially perfect fit is nonsense
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT], data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT]
0 1 essentially perfect fit: summary may be unreliable
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.504e-16 -2.455e-17 -2.110e-18 1.940e-17 5.497e-16
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.331e-15 4.735e-15 7.030e-01 0.4821
currentDF[, TRAIT] 1.000e+00 2.512e-18 3.980e+17 <2e-16 ***
Age 8.907e-20 3.205e-19 2.780e-01 0.7812
Gendermale 3.491e-18 5.603e-18 6.230e-01 0.5335
ORdate_year -1.660e-18 2.363e-18 -7.030e-01 0.4826
Hypertension.compositeyes -2.000e-18 7.335e-18 -2.730e-01 0.7852
DiabetesStatusDiabetes -8.955e-18 6.232e-18 -1.437e+00 0.1514
SmokerStatusEx-smoker 1.792e-18 5.511e-18 3.250e-01 0.7452
SmokerStatusNever smoked 1.381e-17 8.198e-18 1.684e+00 0.0928 .
Med.Statin.LLDyes -4.676e-18 5.742e-18 -8.140e-01 0.4159
Med.all.antiplateletyes -1.084e-18 8.806e-18 -1.230e-01 0.9021
GFR_MDRD 1.582e-20 1.378e-19 1.150e-01 0.9087
BMI 5.057e-19 6.595e-19 7.670e-01 0.4436
MedHx_CVDyes -1.990e-18 5.171e-18 -3.850e-01 0.7005
stenose50-70% -3.220e-17 3.429e-17 -9.390e-01 0.3482
stenose70-90% -1.597e-17 3.185e-17 -5.010e-01 0.6164
stenose90-99% -1.640e-17 3.176e-17 -5.160e-01 0.6058
stenose100% (Occlusion) -1.174e-17 4.033e-17 -2.910e-01 0.7712
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 5.392e-17 on 480 degrees of freedom
Multiple R-squared: 1, Adjusted R-squared: 1
F-statistic: 1.033e+34 on 17 and 480 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MCP1_rank ' .essentially perfect fit: summary may be unreliableCollecting data.essentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableessentially perfect fit: summary may be unreliableWe have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MCP1_rank
Effect size...............: 1
Standard error............: 0
Odds ratio (effect size)..: 2.718
Lower 95% CI..............: 2.718
Upper 95% CI..............: 2.718
T-value...................: 3.98049e+17
P-value...................: 0
R^2.......................: 1
Adjusted r^2..............: 1
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
515.6169 0.3328 0.2191 -0.2574
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.80412 -0.65997 -0.00457 0.55429 2.56164
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.843e+02 7.641e+01 6.338 5.76e-10 ***
currentDF[, TRAIT] 3.256e-01 4.194e-02 7.763 5.86e-14 ***
Age -7.143e-03 5.453e-03 -1.310 0.1909
Gendermale 2.425e-01 9.497e-02 2.553 0.0110 *
ORdate_year -2.411e-01 3.813e-02 -6.322 6.33e-10 ***
Hypertension.compositeyes -1.164e-01 1.248e-01 -0.933 0.3514
DiabetesStatusDiabetes -1.092e-01 1.058e-01 -1.032 0.3027
SmokerStatusEx-smoker -5.274e-03 9.435e-02 -0.056 0.9554
SmokerStatusNever smoked 1.313e-01 1.369e-01 0.959 0.3380
Med.Statin.LLDyes -1.391e-01 9.780e-02 -1.422 0.1557
Med.all.antiplateletyes 6.493e-02 1.529e-01 0.425 0.6712
GFR_MDRD 6.714e-05 2.306e-03 0.029 0.9768
BMI -1.295e-02 1.101e-02 -1.176 0.2403
MedHx_CVDyes 2.487e-02 8.835e-02 0.282 0.7784
stenose50-70% -5.187e-01 5.616e-01 -0.924 0.3562
stenose70-90% -3.000e-01 5.183e-01 -0.579 0.5630
stenose90-99% -3.373e-01 5.164e-01 -0.653 0.5139
stenose100% (Occlusion) -1.088e+00 6.555e-01 -1.660 0.0975 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.876 on 441 degrees of freedom
Multiple R-squared: 0.2302, Adjusted R-squared: 0.2005
F-statistic: 7.756 on 17 and 441 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.325554
Standard error............: 0.041939
Odds ratio (effect size)..: 1.385
Lower 95% CI..............: 1.276
Upper 95% CI..............: 1.503
T-value...................: 7.762627
P-value...................: 5.85751e-14
R^2.......................: 0.230177
Adjusted r^2..............: 0.200501
Sample size of AE DB......: 2423
Sample size of model......: 459
Missing data %............: 81.05654
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
237.9560 0.3123 0.2260 -0.1187 -0.2196
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.12191 -0.54598 -0.01048 0.59883 2.89938
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.159e+02 8.494e+01 2.542 0.0114 *
currentDF[, TRAIT] 3.202e-01 4.603e-02 6.957 1.16e-11 ***
Age -4.036e-03 5.545e-03 -0.728 0.4671
Gendermale 2.294e-01 9.619e-02 2.384 0.0175 *
ORdate_year -1.072e-01 4.239e-02 -2.529 0.0118 *
Hypertension.compositeyes -2.207e-01 1.269e-01 -1.739 0.0828 .
DiabetesStatusDiabetes 1.837e-02 1.079e-01 0.170 0.8649
SmokerStatusEx-smoker 1.135e-01 9.498e-02 1.195 0.2328
SmokerStatusNever smoked 2.227e-01 1.406e-01 1.585 0.1137
Med.Statin.LLDyes -1.420e-01 9.904e-02 -1.434 0.1522
Med.all.antiplateletyes 1.265e-01 1.528e-01 0.828 0.4081
GFR_MDRD 3.131e-04 2.368e-03 0.132 0.8949
BMI -2.044e-02 1.139e-02 -1.795 0.0734 .
MedHx_CVDyes 7.841e-03 8.954e-02 0.088 0.9303
stenose50-70% -5.181e-01 5.901e-01 -0.878 0.3805
stenose70-90% -3.690e-01 5.462e-01 -0.676 0.4996
stenose90-99% -3.060e-01 5.443e-01 -0.562 0.5743
stenose100% (Occlusion) -1.248e+00 6.909e-01 -1.806 0.0715 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9243 on 474 degrees of freedom
Multiple R-squared: 0.1866, Adjusted R-squared: 0.1574
F-statistic: 6.397 on 17 and 474 DF, p-value: 1.141e-13
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.32018
Standard error............: 0.046025
Odds ratio (effect size)..: 1.377
Lower 95% CI..............: 1.259
Upper 95% CI..............: 1.507
T-value...................: 6.956614
P-value...................: 1.163346e-11
R^2.......................: 0.186607
Adjusted r^2..............: 0.157435
Sample size of AE DB......: 2423
Sample size of model......: 492
Missing data %............: 79.69459
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
604.4820 0.2929 0.2398 -0.3016 -0.2271
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.1964 -0.6651 0.0113 0.6225 2.2889
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.803e+02 8.343e+01 6.956 1.18e-11 ***
currentDF[, TRAIT] 2.846e-01 4.567e-02 6.233 1.02e-09 ***
Age -5.768e-03 5.628e-03 -1.025 0.30592
Gendermale 2.556e-01 9.713e-02 2.632 0.00878 **
ORdate_year -2.890e-01 4.164e-02 -6.940 1.30e-11 ***
Hypertension.compositeyes -1.989e-01 1.288e-01 -1.545 0.12313
DiabetesStatusDiabetes -4.949e-02 1.089e-01 -0.454 0.64974
SmokerStatusEx-smoker -2.714e-03 9.658e-02 -0.028 0.97759
SmokerStatusNever smoked 1.362e-01 1.448e-01 0.941 0.34726
Med.Statin.LLDyes -1.385e-01 1.001e-01 -1.384 0.16690
Med.all.antiplateletyes 1.729e-02 1.545e-01 0.112 0.91095
GFR_MDRD 1.248e-04 2.393e-03 0.052 0.95843
BMI -1.447e-02 1.147e-02 -1.261 0.20803
MedHx_CVDyes 1.115e-02 9.069e-02 0.123 0.90223
stenose50-70% -5.050e-01 5.951e-01 -0.849 0.39651
stenose70-90% -3.440e-01 5.507e-01 -0.625 0.53253
stenose90-99% -3.337e-01 5.488e-01 -0.608 0.54347
stenose100% (Occlusion) -1.148e+00 6.963e-01 -1.649 0.09978 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9319 on 470 degrees of freedom
Multiple R-squared: 0.1734, Adjusted R-squared: 0.1435
F-statistic: 5.798 on 17 and 470 DF, p-value: 4.217e-12
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.28463
Standard error............: 0.045667
Odds ratio (effect size)..: 1.329
Lower 95% CI..............: 1.215
Upper 95% CI..............: 1.454
T-value...................: 6.232677
P-value...................: 1.020771e-09
R^2.......................: 0.173361
Adjusted r^2..............: 0.143461
Sample size of AE DB......: 2423
Sample size of model......: 488
Missing data %............: 79.85968
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
445.7351 0.4238 0.2713 -0.2224 -0.1917
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.14418 -0.59601 -0.02638 0.55922 2.18631
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 415.469196 76.990690 5.396 1.13e-07 ***
currentDF[, TRAIT] 0.422204 0.042479 9.939 < 2e-16 ***
Age -0.005294 0.005530 -0.957 0.33890
Gendermale 0.280567 0.093766 2.992 0.00293 **
ORdate_year -0.206761 0.038418 -5.382 1.22e-07 ***
Hypertension.compositeyes -0.168565 0.124543 -1.353 0.17662
DiabetesStatusDiabetes -0.054183 0.106025 -0.511 0.60958
SmokerStatusEx-smoker -0.037196 0.095115 -0.391 0.69594
SmokerStatusNever smoked 0.009232 0.141886 0.065 0.94815
Med.Statin.LLDyes -0.134302 0.097608 -1.376 0.16956
Med.all.antiplateletyes 0.011772 0.147114 0.080 0.93626
GFR_MDRD -0.001411 0.002340 -0.603 0.54677
BMI -0.016033 0.011120 -1.442 0.15009
MedHx_CVDyes 0.063694 0.087746 0.726 0.46830
stenose50-70% -0.347811 0.554132 -0.628 0.53056
stenose70-90% -0.270492 0.513172 -0.527 0.59840
stenose90-99% -0.184304 0.510928 -0.361 0.71848
stenose100% (Occlusion) -0.741191 0.648898 -1.142 0.25400
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8657 on 428 degrees of freedom
Multiple R-squared: 0.2759, Adjusted R-squared: 0.2472
F-statistic: 9.594 on 17 and 428 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: IP10_rank
Effect size...............: 0.422204
Standard error............: 0.042479
Odds ratio (effect size)..: 1.525
Lower 95% CI..............: 1.403
Upper 95% CI..............: 1.658
T-value...................: 9.939162
P-value...................: 4.407968e-21
R^2.......................: 0.27592
Adjusted r^2..............: 0.24716
Sample size of AE DB......: 2423
Sample size of model......: 446
Missing data %............: 81.59307
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
513.0539 0.3244 0.2028 -0.2560 -0.2241
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3999 -0.6942 -0.0328 0.6255 2.3917
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.961e+02 8.030e+01 6.178 1.39e-09 ***
currentDF[, TRAIT] 3.182e-01 4.337e-02 7.337 9.40e-13 ***
Age -5.803e-03 5.532e-03 -1.049 0.2947
Gendermale 2.150e-01 9.642e-02 2.230 0.0262 *
ORdate_year -2.470e-01 4.008e-02 -6.164 1.51e-09 ***
Hypertension.compositeyes -1.928e-01 1.261e-01 -1.529 0.1269
DiabetesStatusDiabetes -7.730e-02 1.073e-01 -0.720 0.4717
SmokerStatusEx-smoker 3.090e-02 9.526e-02 0.324 0.7458
SmokerStatusNever smoked 1.508e-01 1.418e-01 1.063 0.2881
Med.Statin.LLDyes -1.261e-01 9.876e-02 -1.276 0.2024
Med.all.antiplateletyes 2.581e-02 1.523e-01 0.170 0.8655
GFR_MDRD 4.791e-04 2.377e-03 0.202 0.8404
BMI -1.345e-02 1.135e-02 -1.185 0.2368
MedHx_CVDyes 2.438e-02 8.908e-02 0.274 0.7844
stenose50-70% -5.016e-01 5.904e-01 -0.850 0.3959
stenose70-90% -3.122e-01 5.487e-01 -0.569 0.5697
stenose90-99% -3.628e-01 5.470e-01 -0.663 0.5075
stenose100% (Occlusion) -1.110e+00 6.936e-01 -1.601 0.1100
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9289 on 480 degrees of freedom
Multiple R-squared: 0.1889, Adjusted R-squared: 0.1602
F-statistic: 6.577 on 17 and 480 DF, p-value: 3.716e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.318209
Standard error............: 0.043371
Odds ratio (effect size)..: 1.375
Lower 95% CI..............: 1.263
Upper 95% CI..............: 1.497
T-value...................: 7.336843
P-value...................: 9.395425e-13
R^2.......................: 0.188917
Adjusted r^2..............: 0.160192
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + ORdate_year +
Med.Statin.LLD + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] ORdate_year Med.Statin.LLDyes BMI
233.20323 0.27498 -0.11604 -0.26376 -0.01879
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.84543 -0.64613 -0.01877 0.63724 2.65495
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.281e+02 9.923e+01 2.298 0.0220 *
currentDF[, TRAIT] 2.592e-01 4.730e-02 5.480 7.46e-08 ***
Age -4.643e-03 5.932e-03 -0.783 0.4343
Gendermale 1.255e-01 1.036e-01 1.211 0.2266
ORdate_year -1.132e-01 4.954e-02 -2.285 0.0228 *
Hypertension.compositeyes -1.854e-01 1.382e-01 -1.341 0.1806
DiabetesStatusDiabetes -4.917e-02 1.160e-01 -0.424 0.6720
SmokerStatusEx-smoker 1.822e-02 1.039e-01 0.175 0.8609
SmokerStatusNever smoked 1.956e-01 1.483e-01 1.319 0.1877
Med.Statin.LLDyes -2.696e-01 1.098e-01 -2.456 0.0145 *
Med.all.antiplateletyes 4.911e-02 1.622e-01 0.303 0.7622
GFR_MDRD 9.712e-04 2.639e-03 0.368 0.7131
BMI -2.066e-02 1.251e-02 -1.651 0.0994 .
MedHx_CVDyes 9.816e-02 9.763e-02 1.005 0.3153
stenose50-70% -5.757e-01 5.987e-01 -0.961 0.3369
stenose70-90% -3.310e-01 5.520e-01 -0.600 0.5491
stenose90-99% -2.886e-01 5.498e-01 -0.525 0.5999
stenose100% (Occlusion) -1.009e+00 6.988e-01 -1.444 0.1495
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9313 on 409 degrees of freedom
Multiple R-squared: 0.1491, Adjusted R-squared: 0.1137
F-statistic: 4.215 on 17 and 409 DF, p-value: 6.462e-08
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.259178
Standard error............: 0.047297
Odds ratio (effect size)..: 1.296
Lower 95% CI..............: 1.181
Upper 95% CI..............: 1.422
T-value...................: 5.47978
P-value...................: 7.459196e-08
R^2.......................: 0.14908
Adjusted r^2..............: 0.113712
Sample size of AE DB......: 2423
Sample size of model......: 427
Missing data %............: 82.37722
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Med.Statin.LLD + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year Med.Statin.LLDyes
141.838177 0.443802 -0.006814 0.294597 -0.070732 -0.135797
Med.all.antiplateletyes
0.275088
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.04725 -0.57430 -0.01202 0.62814 2.20609
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 152.088685 80.472825 1.890 0.05937 .
currentDF[, TRAIT] 0.437723 0.043170 10.140 < 2e-16 ***
Age -0.007455 0.005274 -1.414 0.15812
Gendermale 0.296610 0.091505 3.241 0.00127 **
ORdate_year -0.075717 0.040148 -1.886 0.05991 .
Hypertension.compositeyes -0.131226 0.120874 -1.086 0.27818
DiabetesStatusDiabetes -0.023393 0.102830 -0.227 0.82014
SmokerStatusEx-smoker 0.078039 0.090790 0.860 0.39046
SmokerStatusNever smoked 0.185541 0.135001 1.374 0.16997
Med.Statin.LLDyes -0.134239 0.094499 -1.421 0.15611
Med.all.antiplateletyes 0.261626 0.145654 1.796 0.07309 .
GFR_MDRD 0.001238 0.002278 0.543 0.58708
BMI -0.006185 0.010877 -0.569 0.56985
MedHx_CVDyes 0.059671 0.085297 0.700 0.48454
stenose50-70% -0.106702 0.566388 -0.188 0.85065
stenose70-90% -0.103307 0.525362 -0.197 0.84419
stenose90-99% -0.122370 0.523727 -0.234 0.81535
stenose100% (Occlusion) -0.262180 0.667961 -0.393 0.69486
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.889 on 480 degrees of freedom
Multiple R-squared: 0.2571, Adjusted R-squared: 0.2308
F-statistic: 9.771 on 17 and 480 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: PARC_rank
Effect size...............: 0.437723
Standard error............: 0.04317
Odds ratio (effect size)..: 1.549
Lower 95% CI..............: 1.423
Upper 95% CI..............: 1.686
T-value...................: 10.13951
P-value...................: 5.072202e-22
R^2.......................: 0.257082
Adjusted r^2..............: 0.230771
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes
278.9982 0.3708 0.2965 -0.1393 -0.1490
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.86874 -0.65351 -0.04043 0.53752 3.05362
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.700e+02 8.015e+01 3.369 0.000822 ***
currentDF[, TRAIT] 3.642e-01 4.388e-02 8.298 1.33e-15 ***
Age -6.351e-03 5.390e-03 -1.178 0.239320
Gendermale 3.220e-01 9.462e-02 3.403 0.000728 ***
ORdate_year -1.342e-01 4.000e-02 -3.356 0.000859 ***
Hypertension.compositeyes -1.157e-01 1.253e-01 -0.924 0.356229
DiabetesStatusDiabetes -7.654e-02 1.069e-01 -0.716 0.474509
SmokerStatusEx-smoker -1.696e-03 9.395e-02 -0.018 0.985607
SmokerStatusNever smoked 1.277e-01 1.365e-01 0.936 0.349882
Med.Statin.LLDyes -1.610e-01 9.799e-02 -1.643 0.101044
Med.all.antiplateletyes 1.145e-01 1.542e-01 0.742 0.458204
GFR_MDRD 8.789e-05 2.324e-03 0.038 0.969846
BMI -1.387e-02 1.103e-02 -1.257 0.209372
MedHx_CVDyes 4.530e-02 8.848e-02 0.512 0.608906
stenose50-70% -4.430e-01 5.608e-01 -0.790 0.429997
stenose70-90% -2.836e-01 5.175e-01 -0.548 0.584037
stenose90-99% -3.073e-01 5.156e-01 -0.596 0.551548
stenose100% (Occlusion) -1.014e+00 6.543e-01 -1.550 0.121818
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8744 on 436 degrees of freedom
Multiple R-squared: 0.241, Adjusted R-squared: 0.2114
F-statistic: 8.141 on 17 and 436 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.364156
Standard error............: 0.043883
Odds ratio (effect size)..: 1.439
Lower 95% CI..............: 1.321
Upper 95% CI..............: 1.569
T-value...................: 8.298409
P-value...................: 1.33225e-15
R^2.......................: 0.240951
Adjusted r^2..............: 0.211355
Sample size of AE DB......: 2423
Sample size of model......: 454
Missing data %............: 81.2629
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Med.Statin.LLD, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Med.Statin.LLDyes
433.8263 0.5797 0.1706 -0.2165 -0.1394
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.5141 -0.5106 -0.0590 0.4883 2.5977
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 427.287820 68.049889 6.279 7.65e-10 ***
currentDF[, TRAIT] 0.573425 0.036693 15.628 < 2e-16 ***
Age -0.002660 0.004737 -0.562 0.5747
Gendermale 0.192328 0.082276 2.338 0.0198 *
ORdate_year -0.212989 0.033962 -6.271 8.00e-10 ***
Hypertension.compositeyes -0.152974 0.107846 -1.418 0.1567
DiabetesStatusDiabetes -0.020392 0.091840 -0.222 0.8244
SmokerStatusEx-smoker -0.009957 0.081469 -0.122 0.9028
SmokerStatusNever smoked 0.102581 0.120853 0.849 0.3964
Med.Statin.LLDyes -0.146496 0.084536 -1.733 0.0837 .
Med.all.antiplateletyes 0.007738 0.129892 0.060 0.9525
GFR_MDRD -0.000838 0.002031 -0.413 0.6800
BMI -0.005220 0.009716 -0.537 0.5913
MedHx_CVDyes 0.066458 0.076394 0.870 0.3848
stenose50-70% -0.106846 0.505468 -0.211 0.8327
stenose70-90% -0.044287 0.469387 -0.094 0.9249
stenose90-99% -0.067123 0.467905 -0.143 0.8860
stenose100% (Occlusion) -0.347525 0.594619 -0.584 0.5592
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7942 on 479 degrees of freedom
Multiple R-squared: 0.4029, Adjusted R-squared: 0.3817
F-statistic: 19.01 on 17 and 479 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: OPG_rank
Effect size...............: 0.573425
Standard error............: 0.036693
Odds ratio (effect size)..: 1.774
Lower 95% CI..............: 1.651
Upper 95% CI..............: 1.907
T-value...................: 15.62752
P-value...................: 8.723821e-45
R^2.......................: 0.402862
Adjusted r^2..............: 0.381669
Sample size of AE DB......: 2423
Sample size of model......: 497
Missing data %............: 79.48824
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
Hypertension.composite + Med.Statin.LLD + Med.all.antiplatelet,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale Hypertension.compositeyes Med.Statin.LLDyes
-0.1133 0.6670 0.2354 -0.1616 -0.1266
Med.all.antiplateletyes
0.1884
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.06932 -0.40155 0.04207 0.43059 2.16413
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 95.840446 66.583223 1.439 0.15069
currentDF[, TRAIT] 0.660727 0.035931 18.389 < 2e-16 ***
Age 0.002250 0.004495 0.501 0.61686
Gendermale 0.224179 0.077357 2.898 0.00393 **
ORdate_year -0.047658 0.033223 -1.434 0.15209
Hypertension.compositeyes -0.137379 0.101881 -1.348 0.17816
DiabetesStatusDiabetes -0.032830 0.086710 -0.379 0.70514
SmokerStatusEx-smoker 0.101332 0.076614 1.323 0.18659
SmokerStatusNever smoked 0.114707 0.114022 1.006 0.31492
Med.Statin.LLDyes -0.110854 0.079774 -1.390 0.16530
Med.all.antiplateletyes 0.177777 0.122479 1.451 0.14730
GFR_MDRD 0.001653 0.001921 0.861 0.38994
BMI -0.013696 0.009170 -1.494 0.13596
MedHx_CVDyes -0.027234 0.072019 -0.378 0.70549
stenose50-70% -0.509971 0.476871 -1.069 0.28542
stenose70-90% -0.355522 0.443195 -0.802 0.42285
stenose90-99% -0.445780 0.441889 -1.009 0.31358
stenose100% (Occlusion) -0.697120 0.560445 -1.244 0.21415
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7503 on 480 degrees of freedom
Multiple R-squared: 0.4708, Adjusted R-squared: 0.452
F-statistic: 25.12 on 17 and 480 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.660727
Standard error............: 0.035931
Odds ratio (effect size)..: 1.936
Lower 95% CI..............: 1.805
Upper 95% CI..............: 2.077
T-value...................: 18.38887
P-value...................: 1.477764e-57
R^2.......................: 0.470782
Adjusted r^2..............: 0.452039
Sample size of AE DB......: 2423
Sample size of model......: 498
Missing data %............: 79.44697
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite + SmokerStatus + BMI,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
657.95447 0.32365 0.21844 -0.32809 -0.25562
SmokerStatusEx-smoker SmokerStatusNever smoked BMI
0.11063 0.30379 -0.01841
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.3008 -0.5935 -0.0500 0.6200 2.4227
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 642.466588 96.040354 6.690 7.88e-11 ***
currentDF[, TRAIT] 0.327490 0.049225 6.653 9.86e-11 ***
Age -0.009134 0.006011 -1.520 0.1294
Gendermale 0.217328 0.105227 2.065 0.0396 *
ORdate_year -0.319786 0.047942 -6.670 8.87e-11 ***
Hypertension.compositeyes -0.226216 0.138958 -1.628 0.1044
DiabetesStatusDiabetes -0.115174 0.117200 -0.983 0.3264
SmokerStatusEx-smoker 0.157607 0.102508 1.538 0.1250
SmokerStatusNever smoked 0.371415 0.156137 2.379 0.0179 *
Med.Statin.LLDyes -0.088078 0.107177 -0.822 0.4117
Med.all.antiplateletyes 0.118234 0.155705 0.759 0.4481
GFR_MDRD -0.001184 0.002523 -0.470 0.6389
BMI -0.021248 0.012637 -1.681 0.0935 .
MedHx_CVDyes 0.126684 0.097007 1.306 0.1924
stenose50-70% -0.356141 0.706324 -0.504 0.6144
stenose70-90% -0.487268 0.656480 -0.742 0.4584
stenose90-99% -0.555999 0.655133 -0.849 0.3966
stenose100% (Occlusion) -1.316272 0.776273 -1.696 0.0908 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9092 on 386 degrees of freedom
Multiple R-squared: 0.1997, Adjusted R-squared: 0.1645
F-statistic: 5.667 on 17 and 386 DF, p-value: 1.641e-11
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.32749
Standard error............: 0.049225
Odds ratio (effect size)..: 1.387
Lower 95% CI..............: 1.26
Upper 95% CI..............: 1.528
T-value...................: 6.652972
P-value...................: 9.858796e-11
R^2.......................: 0.199723
Adjusted r^2..............: 0.164478
Sample size of AE DB......: 2423
Sample size of model......: 404
Missing data %............: 83.32645
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year + Hypertension.composite, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year Hypertension.compositeyes
394.9850 0.1173 0.3072 -0.1971 -0.1996
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4029 -0.6503 -0.0023 0.6538 2.5270
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 3.727e+02 8.640e+01 4.314 1.97e-05 ***
currentDF[, TRAIT] 1.151e-01 4.569e-02 2.518 0.01213 *
Age -7.562e-03 5.876e-03 -1.287 0.19879
Gendermale 3.330e-01 1.023e-01 3.255 0.00122 **
ORdate_year -1.855e-01 4.312e-02 -4.301 2.08e-05 ***
Hypertension.compositeyes -2.047e-01 1.333e-01 -1.536 0.12529
DiabetesStatusDiabetes -7.342e-02 1.152e-01 -0.638 0.52405
SmokerStatusEx-smoker 9.055e-02 1.010e-01 0.897 0.37036
SmokerStatusNever smoked 3.343e-01 1.514e-01 2.209 0.02767 *
Med.Statin.LLDyes -1.448e-01 1.057e-01 -1.370 0.17140
Med.all.antiplateletyes 1.293e-01 1.600e-01 0.808 0.41953
GFR_MDRD 6.369e-05 2.538e-03 0.025 0.97999
BMI -1.626e-02 1.211e-02 -1.343 0.18005
MedHx_CVDyes 7.531e-02 9.475e-02 0.795 0.42712
stenose50-70% -5.266e-01 6.146e-01 -0.857 0.39202
stenose70-90% -2.725e-01 5.712e-01 -0.477 0.63351
stenose90-99% -2.543e-01 5.695e-01 -0.446 0.65547
stenose100% (Occlusion) -1.070e+00 7.220e-01 -1.481 0.13916
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9661 on 456 degrees of freedom
Multiple R-squared: 0.1171, Adjusted R-squared: 0.08417
F-statistic: 3.557 on 17 and 456 DF, p-value: 2.502e-06
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.115068
Standard error............: 0.045691
Odds ratio (effect size)..: 1.122
Lower 95% CI..............: 1.026
Upper 95% CI..............: 1.227
T-value...................: 2.518373
P-value...................: 0.0121311
R^2.......................: 0.117082
Adjusted r^2..............: 0.084167
Sample size of AE DB......: 2423
Sample size of model......: 474
Missing data %............: 80.43747
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Gender +
ORdate_year, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Gendermale ORdate_year
304.4297 0.3750 0.3703 -0.1520
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.4837 -0.5718 -0.0139 0.6128 2.4435
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.849e+02 8.571e+01 3.324 0.000960 ***
currentDF[, TRAIT] 3.568e-01 4.678e-02 7.627 1.39e-13 ***
Age -7.665e-03 5.601e-03 -1.369 0.171785
Gendermale 3.938e-01 9.732e-02 4.046 6.11e-05 ***
ORdate_year -1.419e-01 4.277e-02 -3.318 0.000979 ***
Hypertension.compositeyes -9.213e-02 1.296e-01 -0.711 0.477414
DiabetesStatusDiabetes -1.266e-02 1.097e-01 -0.115 0.908206
SmokerStatusEx-smoker 9.316e-03 9.652e-02 0.097 0.923151
SmokerStatusNever smoked 1.612e-01 1.431e-01 1.126 0.260718
Med.Statin.LLDyes -1.025e-01 9.881e-02 -1.038 0.299983
Med.all.antiplateletyes 1.319e-01 1.559e-01 0.846 0.398008
GFR_MDRD -6.253e-04 2.447e-03 -0.256 0.798406
BMI -1.121e-02 1.171e-02 -0.958 0.338746
MedHx_CVDyes 2.992e-02 9.069e-02 0.330 0.741621
stenose50-70% -2.844e-03 5.902e-01 -0.005 0.996157
stenose70-90% 9.290e-02 5.475e-01 0.170 0.865339
stenose90-99% 1.044e-01 5.459e-01 0.191 0.848380
stenose100% (Occlusion) -6.307e-01 6.909e-01 -0.913 0.361779
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9222 on 459 degrees of freedom
Multiple R-squared: 0.1955, Adjusted R-squared: 0.1657
F-statistic: 6.56 on 17 and 459 DF, p-value: 4.844e-14
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP2_rank
Effect size...............: 0.3568
Standard error............: 0.046779
Odds ratio (effect size)..: 1.429
Lower 95% CI..............: 1.304
Upper 95% CI..............: 1.566
T-value...................: 7.627405
P-value...................: 1.392688e-13
R^2.......................: 0.195463
Adjusted r^2..............: 0.165666
Sample size of AE DB......: 2423
Sample size of model......: 477
Missing data %............: 80.31366
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + SmokerStatus +
Med.all.antiplatelet + BMI + stenose, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
407.49841 0.47258 -0.01079 0.14007 -0.20233
Hypertension.compositeyes SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes BMI
-0.30045 0.17313 0.32439 0.24655 -0.03177
stenose50-70% stenose70-90% stenose90-99% stenose100% (Occlusion)
-0.80804 -0.68502 -0.60890 -1.62204
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.06911 -0.54053 0.02395 0.54548 2.68890
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 396.174815 79.386931 4.990 8.57e-07 ***
currentDF[, TRAIT] 0.473215 0.041069 11.522 < 2e-16 ***
Age -0.010761 0.005218 -2.062 0.03974 *
Gendermale 0.131128 0.092224 1.422 0.15575
ORdate_year -0.196685 0.039619 -4.964 9.73e-07 ***
Hypertension.compositeyes -0.278728 0.119263 -2.337 0.01986 *
DiabetesStatusDiabetes -0.024611 0.102482 -0.240 0.81032
SmokerStatusEx-smoker 0.177017 0.090168 1.963 0.05023 .
SmokerStatusNever smoked 0.323357 0.132778 2.435 0.01526 *
Med.Statin.LLDyes -0.090403 0.092376 -0.979 0.32827
Med.all.antiplateletyes 0.248239 0.145528 1.706 0.08873 .
GFR_MDRD 0.001309 0.002298 0.570 0.56911
BMI -0.031639 0.011018 -2.871 0.00428 **
MedHx_CVDyes 0.023068 0.084768 0.272 0.78565
stenose50-70% -0.861245 0.549700 -1.567 0.11786
stenose70-90% -0.721187 0.512264 -1.408 0.15985
stenose90-99% -0.651536 0.509866 -1.278 0.20195
stenose100% (Occlusion) -1.677012 0.647752 -2.589 0.00993 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8621 on 459 degrees of freedom
Multiple R-squared: 0.2969, Adjusted R-squared: 0.2708
F-statistic: 11.4 on 17 and 459 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.473215
Standard error............: 0.041069
Odds ratio (effect size)..: 1.605
Lower 95% CI..............: 1.481
Upper 95% CI..............: 1.74
T-value...................: 11.52248
P-value...................: 3.710762e-27
R^2.......................: 0.296872
Adjusted r^2..............: 0.27083
Sample size of AE DB......: 2423
Sample size of model......: 477
Missing data %............: 80.31366
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + SmokerStatus +
Med.all.antiplatelet + BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] Age Gendermale ORdate_year
261.74610 0.60020 -0.01143 0.21359 -0.13001
Hypertension.compositeyes SmokerStatusEx-smoker SmokerStatusNever smoked Med.all.antiplateletyes BMI
-0.18921 0.13122 0.23333 0.32574 -0.03044
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-3.07159 -0.47744 -0.02571 0.49342 2.79265
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 255.302470 72.052050 3.543 0.000436 ***
currentDF[, TRAIT] 0.602903 0.037128 16.238 < 2e-16 ***
Age -0.011154 0.004713 -2.366 0.018380 *
Gendermale 0.202433 0.082269 2.461 0.014238 *
ORdate_year -0.126662 0.035959 -3.522 0.000471 ***
Hypertension.compositeyes -0.169317 0.108823 -1.556 0.120423
DiabetesStatusDiabetes 0.007354 0.092609 0.079 0.936743
SmokerStatusEx-smoker 0.139631 0.081284 1.718 0.086506 .
SmokerStatusNever smoked 0.244867 0.120009 2.040 0.041883 *
Med.Statin.LLDyes -0.106001 0.083422 -1.271 0.204497
Med.all.antiplateletyes 0.276223 0.131469 2.101 0.036183 *
GFR_MDRD 0.001761 0.002075 0.849 0.396498
BMI -0.033145 0.009935 -3.336 0.000919 ***
MedHx_CVDyes 0.047506 0.076557 0.621 0.535216
stenose50-70% -0.296349 0.495684 -0.598 0.550230
stenose70-90% -0.238666 0.460977 -0.518 0.604889
stenose90-99% -0.260377 0.459383 -0.567 0.571131
stenose100% (Occlusion) -1.037848 0.582274 -1.782 0.075346 .
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.7786 on 458 degrees of freedom
Multiple R-squared: 0.4262, Adjusted R-squared: 0.4049
F-statistic: 20.01 on 17 and 458 DF, p-value: < 2.2e-16
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_rank
Trait/outcome.............: MMP9_rank
Effect size...............: 0.602903
Standard error............: 0.037128
Odds ratio (effect size)..: 1.827
Lower 95% CI..............: 1.699
Upper 95% CI..............: 1.965
T-value...................: 16.2383
P-value...................: 3.674529e-47
R^2.......................: 0.426227
Adjusted r^2..............: 0.40493
Sample size of AE DB......: 2423
Sample size of model......: 476
Missing data %............: 80.35493
Analysis of MCP1_plasma_olink_rank.
- processing IL2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] GFR_MDRD
0.81116 -0.17540 -0.01086
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.01057 -0.55353 -0.02598 0.53951 2.36127
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -46.109252 220.953936 -0.209 0.835
currentDF[, TRAIT] -0.166300 0.113110 -1.470 0.145
Age -0.004869 0.013629 -0.357 0.722
Gendermale 0.109903 0.232709 0.472 0.638
ORdate_year 0.024035 0.110101 0.218 0.828
Hypertension.compositeyes -0.033523 0.259514 -0.129 0.898
DiabetesStatusDiabetes 0.100091 0.276170 0.362 0.718
SmokerStatusEx-smoker 0.167771 0.216832 0.774 0.441
SmokerStatusNever smoked 0.503057 0.368707 1.364 0.176
Med.Statin.LLDyes -0.217166 0.215627 -1.007 0.317
Med.all.antiplateletyes -0.120974 0.381036 -0.317 0.752
GFR_MDRD -0.010495 0.007101 -1.478 0.143
BMI 0.006729 0.032426 0.208 0.836
MedHx_CVDyes 0.107853 0.228611 0.472 0.638
stenose50-70% -2.337167 1.488698 -1.570 0.120
stenose70-90% -1.030789 1.007372 -1.023 0.309
stenose90-99% -1.166742 0.999878 -1.167 0.246
Residual standard error: 0.9459 on 86 degrees of freedom
Multiple R-squared: 0.141, Adjusted R-squared: -0.01887
F-statistic: 0.8819 on 16 and 86 DF, p-value: 0.5914
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL2_rank
Effect size...............: -0.1663
Standard error............: 0.11311
Odds ratio (effect size)..: 0.847
Lower 95% CI..............: 0.678
Upper 95% CI..............: 1.057
T-value...................: -1.470253
P-value...................: 0.1451424
R^2.......................: 0.140951
Adjusted r^2..............: -0.018872
Sample size of AE DB......: 2423
Sample size of model......: 103
Missing data %............: 95.74907
- processing IL4_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.17955 -0.01661
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.13637 -0.52962 0.02432 0.58833 2.26962
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 68.160543 208.381001 0.327 0.7444
currentDF[, TRAIT] -0.047778 0.106469 -0.449 0.6547
Age -0.001212 0.012869 -0.094 0.9252
Gendermale 0.105459 0.242794 0.434 0.6651
ORdate_year -0.034191 0.103904 -0.329 0.7429
Hypertension.compositeyes -0.061321 0.258422 -0.237 0.8130
DiabetesStatusDiabetes 0.121243 0.267539 0.453 0.6516
SmokerStatusEx-smoker 0.162829 0.204761 0.795 0.4287
SmokerStatusNever smoked 0.484717 0.348679 1.390 0.1681
Med.Statin.LLDyes -0.031691 0.206961 -0.153 0.8787
Med.all.antiplateletyes -0.245396 0.349070 -0.703 0.4840
GFR_MDRD -0.014100 0.007285 -1.935 0.0562 .
BMI 0.014667 0.029947 0.490 0.6255
MedHx_CVDyes 0.152231 0.216828 0.702 0.4845
stenose70-90% 1.018902 1.041120 0.979 0.3305
stenose90-99% 1.008607 1.044180 0.966 0.3368
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9075 on 86 degrees of freedom
Multiple R-squared: 0.1334, Adjusted R-squared: -0.0178
F-statistic: 0.8822 on 15 and 86 DF, p-value: 0.5858
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL4_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL4_rank
Effect size...............: -0.047778
Standard error............: 0.106469
Odds ratio (effect size)..: 0.953
Lower 95% CI..............: 0.774
Upper 95% CI..............: 1.175
T-value...................: -0.448748
P-value...................: 0.6547423
R^2.......................: 0.133357
Adjusted r^2..............: -0.017802
Sample size of AE DB......: 2423
Sample size of model......: 102
Missing data %............: 95.79034
- processing IL5_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] GFR_MDRD
0.623461 -0.202543 -0.008233
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.95526 -0.52301 -0.01455 0.47792 2.10467
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.716e+01 2.136e+02 -0.408 0.6843
currentDF[, TRAIT] -2.166e-01 1.063e-01 -2.037 0.0446 *
Age -2.966e-05 1.348e-02 -0.002 0.9982
Gendermale 9.871e-02 2.284e-01 0.432 0.6666
ORdate_year 4.286e-02 1.065e-01 0.402 0.6884
Hypertension.compositeyes 6.568e-02 2.489e-01 0.264 0.7925
DiabetesStatusDiabetes 2.006e-02 2.695e-01 0.074 0.9408
SmokerStatusEx-smoker 1.857e-01 2.161e-01 0.859 0.3926
SmokerStatusNever smoked 4.687e-01 3.662e-01 1.280 0.2039
Med.Statin.LLDyes -1.425e-01 2.066e-01 -0.690 0.4922
Med.all.antiplateletyes -1.668e-01 3.566e-01 -0.468 0.6410
GFR_MDRD -5.484e-03 7.109e-03 -0.771 0.4425
BMI 1.981e-02 2.879e-02 0.688 0.4932
MedHx_CVDyes 1.339e-01 2.115e-01 0.633 0.5282
stenose70-90% 1.129e+00 1.065e+00 1.060 0.2920
stenose90-99% 1.073e+00 1.074e+00 0.999 0.3204
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9413 on 89 degrees of freedom
Multiple R-squared: 0.1258, Adjusted R-squared: -0.02158
F-statistic: 0.8535 on 15 and 89 DF, p-value: 0.6168
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL5_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL5_rank
Effect size...............: -0.216555
Standard error............: 0.106311
Odds ratio (effect size)..: 0.805
Lower 95% CI..............: 0.654
Upper 95% CI..............: 0.992
T-value...................: -2.036993
P-value...................: 0.04462147
R^2.......................: 0.125763
Adjusted r^2..............: -0.021581
Sample size of AE DB......: 2423
Sample size of model......: 105
Missing data %............: 95.66653
- processing IL6_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ Gender + GFR_MDRD + BMI,
data = currentDF)
Coefficients:
(Intercept) Gendermale GFR_MDRD BMI
-0.437786 0.279390 -0.009769 0.037635
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.9058 -0.5835 0.0000 0.5142 2.2702
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.002e+02 2.144e+02 -0.467 0.641
currentDF[, TRAIT] -1.898e-02 1.057e-01 -0.180 0.858
Age -2.249e-03 1.357e-02 -0.166 0.869
Gendermale 1.922e-01 2.286e-01 0.841 0.403
ORdate_year 5.066e-02 1.068e-01 0.474 0.637
Hypertension.compositeyes -7.004e-02 2.500e-01 -0.280 0.780
DiabetesStatusDiabetes 1.487e-01 2.565e-01 0.580 0.564
SmokerStatusEx-smoker 1.553e-01 2.128e-01 0.730 0.468
SmokerStatusNever smoked 3.705e-01 3.535e-01 1.048 0.298
Med.Statin.LLDyes -1.423e-01 2.040e-01 -0.697 0.488
Med.all.antiplateletyes -6.283e-02 3.431e-01 -0.183 0.855
GFR_MDRD -7.760e-03 7.005e-03 -1.108 0.271
BMI 1.959e-02 2.516e-02 0.779 0.438
MedHx_CVDyes 2.825e-01 2.155e-01 1.311 0.193
stenose50-70% -2.757e+00 1.411e+00 -1.954 0.054 .
stenose70-90% -1.217e+00 9.803e-01 -1.242 0.218
stenose90-99% -1.278e+00 9.723e-01 -1.315 0.192
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9157 on 86 degrees of freedom
Multiple R-squared: 0.1477, Adjusted R-squared: -0.01084
F-statistic: 0.9316 on 16 and 86 DF, p-value: 0.5369
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL6_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL6_rank
Effect size...............: -0.018977
Standard error............: 0.105677
Odds ratio (effect size)..: 0.981
Lower 95% CI..............: 0.798
Upper 95% CI..............: 1.207
T-value...................: -0.179571
P-value...................: 0.8579115
R^2.......................: 0.147724
Adjusted r^2..............: -0.010838
Sample size of AE DB......: 2423
Sample size of model......: 103
Missing data %............: 95.74907
- processing IL8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
0.97041 -0.01294
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.93039 -0.56878 -0.00704 0.48556 2.57505
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.551e+01 1.988e+02 -0.279 0.7807
currentDF[, TRAIT] -2.170e-02 1.001e-01 -0.217 0.8288
Age 1.993e-04 1.296e-02 0.015 0.9878
Gendermale 2.567e-01 2.230e-01 1.151 0.2530
ORdate_year 2.864e-02 9.910e-02 0.289 0.7733
Hypertension.compositeyes 5.908e-02 2.564e-01 0.230 0.8183
DiabetesStatusDiabetes 1.626e-01 2.611e-01 0.623 0.5351
SmokerStatusEx-smoker 3.003e-02 2.116e-01 0.142 0.8875
SmokerStatusNever smoked 3.717e-01 3.774e-01 0.985 0.3275
Med.Statin.LLDyes 3.319e-03 2.106e-01 0.016 0.9875
Med.all.antiplateletyes -3.580e-01 3.587e-01 -0.998 0.3211
GFR_MDRD -1.097e-02 6.497e-03 -1.688 0.0951 .
BMI 1.560e-02 2.565e-02 0.608 0.5447
MedHx_CVDyes -8.887e-02 2.159e-01 -0.412 0.6817
stenose70-90% -1.286e+00 9.653e-01 -1.333 0.1862
stenose90-99% -1.462e+00 9.630e-01 -1.518 0.1327
stenose100% (Occlusion) -1.235e+00 1.359e+00 -0.909 0.3660
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9302 on 85 degrees of freedom
Multiple R-squared: 0.1351, Adjusted R-squared: -0.02771
F-statistic: 0.8298 on 16 and 85 DF, p-value: 0.649
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL8_rank
Effect size...............: -0.021698
Standard error............: 0.100058
Odds ratio (effect size)..: 0.979
Lower 95% CI..............: 0.804
Upper 95% CI..............: 1.191
T-value...................: -0.216857
P-value...................: 0.8288393
R^2.......................: 0.135094
Adjusted r^2..............: -0.027712
Sample size of AE DB......: 2423
Sample size of model......: 102
Missing data %............: 95.79034
- processing IL9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.18287 -0.01524
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.02862 -0.56904 -0.01368 0.61609 2.47130
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -56.926882 155.877905 -0.365 0.7157
currentDF[, TRAIT] 0.059252 0.105548 0.561 0.5758
Age 0.002052 0.012126 0.169 0.8660
Gendermale 0.069865 0.202926 0.344 0.7313
ORdate_year 0.029680 0.077633 0.382 0.7030
Hypertension.compositeyes 0.172614 0.225676 0.765 0.4461
DiabetesStatusDiabetes 0.304248 0.224808 1.353 0.1790
SmokerStatusEx-smoker -0.008080 0.207139 -0.039 0.9690
SmokerStatusNever smoked 0.156490 0.332080 0.471 0.6385
Med.Statin.LLDyes -0.176254 0.212734 -0.829 0.4093
Med.all.antiplateletyes -0.480954 0.398367 -1.207 0.2301
GFR_MDRD -0.013004 0.005617 -2.315 0.0226 *
BMI 0.004347 0.022253 0.195 0.8455
MedHx_CVDyes 0.042893 0.199643 0.215 0.8303
stenose50-70% -0.538106 1.357676 -0.396 0.6927
stenose70-90% -1.345401 0.965176 -1.394 0.1664
stenose90-99% -1.574180 0.967472 -1.627 0.1068
stenose100% (Occlusion) -1.202558 1.323597 -0.909 0.3657
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.931 on 101 degrees of freedom
Multiple R-squared: 0.159, Adjusted R-squared: 0.01745
F-statistic: 1.123 on 17 and 101 DF, p-value: 0.343
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL9_rank
Effect size...............: 0.059252
Standard error............: 0.105548
Odds ratio (effect size)..: 1.061
Lower 95% CI..............: 0.863
Upper 95% CI..............: 1.305
T-value...................: 0.561371
P-value...................: 0.575788
R^2.......................: 0.159001
Adjusted r^2..............: 0.017446
Sample size of AE DB......: 2423
Sample size of model......: 119
Missing data %............: 95.08873
- processing IL10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD + BMI, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD BMI
0.14139 -0.01557 0.03508
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.05936 -0.54725 -0.00857 0.49024 1.87269
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 131.142636 225.032294 0.583 0.562
currentDF[, TRAIT] -0.065515 0.105804 -0.619 0.538
Age -0.001050 0.012812 -0.082 0.935
Gendermale 0.180994 0.225396 0.803 0.424
ORdate_year -0.065732 0.112141 -0.586 0.559
Hypertension.compositeyes -0.113544 0.245443 -0.463 0.645
DiabetesStatusDiabetes 0.070500 0.262474 0.269 0.789
SmokerStatusEx-smoker 0.071905 0.203917 0.353 0.725
SmokerStatusNever smoked 0.451593 0.333741 1.353 0.180
Med.Statin.LLDyes -0.119197 0.204890 -0.582 0.562
Med.all.antiplateletyes -0.172398 0.350679 -0.492 0.624
GFR_MDRD -0.014224 0.006949 -2.047 0.044 *
BMI 0.027292 0.029740 0.918 0.362
MedHx_CVDyes 0.065037 0.213753 0.304 0.762
stenose70-90% 0.867352 0.997041 0.870 0.387
stenose90-99% 0.950746 1.002811 0.948 0.346
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8669 on 80 degrees of freedom
Multiple R-squared: 0.1626, Adjusted R-squared: 0.00562
F-statistic: 1.036 on 15 and 80 DF, p-value: 0.4289
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL10_rank
Effect size...............: -0.065515
Standard error............: 0.105804
Odds ratio (effect size)..: 0.937
Lower 95% CI..............: 0.761
Upper 95% CI..............: 1.152
T-value...................: -0.619213
P-value...................: 0.5375354
R^2.......................: 0.162628
Adjusted r^2..............: 0.00562
Sample size of AE DB......: 2423
Sample size of model......: 96
Missing data %............: 96.03797
- processing IL12_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] GFR_MDRD
1.01443 -0.13957 -0.01384
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.05487 -0.54994 -0.02101 0.51977 2.15950
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -25.664286 234.698043 -0.109 0.913
currentDF[, TRAIT] -0.146078 0.117402 -1.244 0.217
Age -0.007234 0.013897 -0.521 0.604
Gendermale 0.166039 0.230039 0.722 0.472
ORdate_year 0.012790 0.116910 0.109 0.913
Hypertension.compositeyes 0.022157 0.266237 0.083 0.934
DiabetesStatusDiabetes 0.040332 0.271921 0.148 0.882
SmokerStatusEx-smoker 0.290335 0.220310 1.318 0.191
SmokerStatusNever smoked 0.521503 0.358398 1.455 0.149
Med.Statin.LLDyes -0.017059 0.217635 -0.078 0.938
Med.all.antiplateletyes -0.185242 0.353073 -0.525 0.601
GFR_MDRD -0.013488 0.007506 -1.797 0.076 .
BMI 0.016101 0.031315 0.514 0.609
MedHx_CVDyes 0.029296 0.231281 0.127 0.900
stenose70-90% 0.946397 1.077747 0.878 0.382
stenose90-99% 0.902003 1.092528 0.826 0.411
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9209 on 82 degrees of freedom
Multiple R-squared: 0.1413, Adjusted R-squared: -0.01581
F-statistic: 0.8994 on 15 and 82 DF, p-value: 0.5675
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL12_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL12_rank
Effect size...............: -0.146078
Standard error............: 0.117402
Odds ratio (effect size)..: 0.864
Lower 95% CI..............: 0.686
Upper 95% CI..............: 1.088
T-value...................: -1.244262
P-value...................: 0.216948
R^2.......................: 0.141277
Adjusted r^2..............: -0.015807
Sample size of AE DB......: 2423
Sample size of model......: 98
Missing data %............: 95.95543
- processing IL13_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.3108 -0.0174
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.17909 -0.59609 0.00429 0.55902 2.67512
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.637e+01 1.516e+02 -0.240 0.81077
currentDF[, TRAIT] 7.369e-02 9.063e-02 0.813 0.41786
Age 7.898e-04 1.103e-02 0.072 0.94302
Gendermale 9.590e-02 1.895e-01 0.506 0.61377
ORdate_year 1.933e-02 7.548e-02 0.256 0.79836
Hypertension.compositeyes 1.351e-01 2.126e-01 0.635 0.52643
DiabetesStatusDiabetes 1.546e-01 2.077e-01 0.744 0.45810
SmokerStatusEx-smoker 8.432e-03 1.884e-01 0.045 0.96439
SmokerStatusNever smoked 1.933e-01 3.242e-01 0.596 0.55231
Med.Statin.LLDyes -6.918e-02 1.919e-01 -0.360 0.71916
Med.all.antiplateletyes -1.813e-01 3.016e-01 -0.601 0.54906
GFR_MDRD -1.620e-02 5.387e-03 -3.007 0.00324 **
BMI 6.539e-03 2.114e-02 0.309 0.75759
MedHx_CVDyes 7.598e-02 1.835e-01 0.414 0.67961
stenose50-70% -1.392e+00 1.169e+00 -1.190 0.23647
stenose70-90% -1.275e+00 9.528e-01 -1.339 0.18333
stenose90-99% -1.520e+00 9.537e-01 -1.594 0.11364
stenose100% (Occlusion) -1.211e+00 1.314e+00 -0.922 0.35848
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9242 on 115 degrees of freedom
Multiple R-squared: 0.1547, Adjusted R-squared: 0.02977
F-statistic: 1.238 on 17 and 115 DF, p-value: 0.2467
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL13_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL13_rank
Effect size...............: 0.07369
Standard error............: 0.090633
Odds ratio (effect size)..: 1.076
Lower 95% CI..............: 0.901
Upper 95% CI..............: 1.286
T-value...................: 0.813058
P-value...................: 0.4178648
R^2.......................: 0.154725
Adjusted r^2..............: 0.029771
Sample size of AE DB......: 2423
Sample size of model......: 133
Missing data %............: 94.51094
- processing IL21_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.3108 -0.0174
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.1833 -0.6015 0.0000 0.5600 2.6811
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -42.584622 151.060425 -0.282 0.77852
currentDF[, TRAIT] 0.095314 0.087326 1.091 0.27735
Age 0.000785 0.010985 0.071 0.94316
Gendermale 0.087433 0.189366 0.462 0.64516
ORdate_year 0.022472 0.075239 0.299 0.76573
Hypertension.compositeyes 0.132587 0.211748 0.626 0.53245
DiabetesStatusDiabetes 0.150070 0.207287 0.724 0.47055
SmokerStatusEx-smoker 0.014017 0.187902 0.075 0.94066
SmokerStatusNever smoked 0.183578 0.323727 0.567 0.57177
Med.Statin.LLDyes -0.069801 0.190999 -0.365 0.71545
Med.all.antiplateletyes -0.186215 0.300982 -0.619 0.53734
GFR_MDRD -0.016293 0.005376 -3.031 0.00301 **
BMI 0.005706 0.021119 0.270 0.78752
MedHx_CVDyes 0.083958 0.183416 0.458 0.64800
stenose50-70% -1.459175 1.170343 -1.247 0.21501
stenose70-90% -1.320332 0.952580 -1.386 0.16841
stenose90-99% -1.572519 0.953777 -1.649 0.10193
stenose100% (Occlusion) -1.220177 1.309789 -0.932 0.35350
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9221 on 115 degrees of freedom
Multiple R-squared: 0.1586, Adjusted R-squared: 0.0342
F-statistic: 1.275 on 17 and 115 DF, p-value: 0.2211
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IL21_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IL21_rank
Effect size...............: 0.095314
Standard error............: 0.087326
Odds ratio (effect size)..: 1.1
Lower 95% CI..............: 0.927
Upper 95% CI..............: 1.305
T-value...................: 1.091469
P-value...................: 0.277348
R^2.......................: 0.158582
Adjusted r^2..............: 0.034198
Sample size of AE DB......: 2423
Sample size of model......: 133
Missing data %............: 94.51094
- processing INFG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD + BMI, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD BMI
0.26426 -0.01842 0.03916
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.18492 -0.52504 -0.02817 0.50640 1.87246
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 135.487774 206.197250 0.657 0.51289
currentDF[, TRAIT] -0.128675 0.116590 -1.104 0.27282
Age -0.005634 0.011538 -0.488 0.62658
Gendermale 0.244724 0.208039 1.176 0.24271
ORdate_year -0.066694 0.102798 -0.649 0.51820
Hypertension.compositeyes 0.019508 0.232682 0.084 0.93338
DiabetesStatusDiabetes 0.103540 0.236337 0.438 0.66241
SmokerStatusEx-smoker 0.065103 0.189836 0.343 0.73248
SmokerStatusNever smoked 0.413191 0.345215 1.197 0.23463
Med.Statin.LLDyes -0.029079 0.202359 -0.144 0.88607
Med.all.antiplateletyes -0.158282 0.311183 -0.509 0.61230
GFR_MDRD -0.017755 0.006142 -2.890 0.00487 **
BMI 0.034901 0.024490 1.425 0.15774
MedHx_CVDyes -0.005517 0.208196 -0.027 0.97892
stenose50-70% -1.689022 1.134954 -1.488 0.14036
stenose70-90% -1.330221 0.904302 -1.471 0.14494
stenose90-99% -1.257411 0.896647 -1.402 0.16441
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8571 on 86 degrees of freedom
Multiple R-squared: 0.2342, Adjusted R-squared: 0.09173
F-statistic: 1.644 on 16 and 86 DF, p-value: 0.07446
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' INFG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: INFG_rank
Effect size...............: -0.128675
Standard error............: 0.11659
Odds ratio (effect size)..: 0.879
Lower 95% CI..............: 0.7
Upper 95% CI..............: 1.105
T-value...................: -1.10365
P-value...................: 0.2728237
R^2.......................: 0.234204
Adjusted r^2..............: 0.091731
Sample size of AE DB......: 2423
Sample size of model......: 103
Missing data %............: 95.74907
- processing TNFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD + BMI, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD BMI
0.28432 -0.01658 0.03476
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.08701 -0.51859 0.01372 0.54943 2.15003
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 188.621096 205.642066 0.917 0.3617
currentDF[, TRAIT] -0.102401 0.106436 -0.962 0.3388
Age -0.002801 0.011992 -0.234 0.8159
Gendermale 0.047208 0.215341 0.219 0.8270
ORdate_year -0.094200 0.102519 -0.919 0.3609
Hypertension.compositeyes -0.088498 0.244246 -0.362 0.7180
DiabetesStatusDiabetes 0.164325 0.245112 0.670 0.5045
SmokerStatusEx-smoker 0.216085 0.201820 1.071 0.2875
SmokerStatusNever smoked 0.489164 0.330627 1.480 0.1428
Med.Statin.LLDyes -0.097734 0.200695 -0.487 0.6276
Med.all.antiplateletyes -0.262662 0.303010 -0.867 0.3886
GFR_MDRD -0.015850 0.006569 -2.413 0.0181 *
BMI 0.016403 0.028173 0.582 0.5620
MedHx_CVDyes 0.187501 0.209443 0.895 0.3733
stenose70-90% 1.050133 0.980538 1.071 0.2873
stenose90-99% 1.032196 0.984150 1.049 0.2973
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8568 on 82 degrees of freedom
Multiple R-squared: 0.1876, Adjusted R-squared: 0.03904
F-statistic: 1.263 on 15 and 82 DF, p-value: 0.2449
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' TNFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: TNFA_rank
Effect size...............: -0.102401
Standard error............: 0.106436
Odds ratio (effect size)..: 0.903
Lower 95% CI..............: 0.733
Upper 95% CI..............: 1.112
T-value...................: -0.962093
P-value...................: 0.3388313
R^2.......................: 0.187638
Adjusted r^2..............: 0.039035
Sample size of AE DB......: 2423
Sample size of model......: 98
Missing data %............: 95.95543
- processing MIF_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.3108 -0.0174
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.114 -0.581 0.000 0.597 2.725
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.255e+01 1.674e+02 -0.433 0.66552
currentDF[, TRAIT] 4.386e-02 1.010e-01 0.434 0.66487
Age 4.828e-04 1.105e-02 0.044 0.96523
Gendermale 1.035e-01 1.899e-01 0.545 0.58679
ORdate_year 3.733e-02 8.339e-02 0.448 0.65526
Hypertension.compositeyes 1.372e-01 2.149e-01 0.638 0.52453
DiabetesStatusDiabetes 1.514e-01 2.084e-01 0.726 0.46912
SmokerStatusEx-smoker 1.373e-02 1.887e-01 0.073 0.94213
SmokerStatusNever smoked 2.126e-01 3.242e-01 0.656 0.51336
Med.Statin.LLDyes -4.262e-02 1.923e-01 -0.222 0.82500
Med.all.antiplateletyes -1.805e-01 3.022e-01 -0.597 0.55143
GFR_MDRD -1.615e-02 5.401e-03 -2.990 0.00342 **
BMI 6.827e-03 2.122e-02 0.322 0.74831
MedHx_CVDyes 5.675e-02 1.850e-01 0.307 0.75962
stenose50-70% -1.264e+00 1.178e+00 -1.073 0.28550
stenose70-90% -1.192e+00 9.517e-01 -1.252 0.21300
stenose90-99% -1.421e+00 9.473e-01 -1.500 0.13628
stenose100% (Occlusion) -1.256e+00 1.315e+00 -0.955 0.34145
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9261 on 115 degrees of freedom
Multiple R-squared: 0.1513, Adjusted R-squared: 0.02579
F-statistic: 1.206 on 17 and 115 DF, p-value: 0.2713
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MIF_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MIF_rank
Effect size...............: 0.043856
Standard error............: 0.100978
Odds ratio (effect size)..: 1.045
Lower 95% CI..............: 0.857
Upper 95% CI..............: 1.274
T-value...................: 0.434317
P-value...................: 0.6648717
R^2.......................: 0.151258
Adjusted r^2..............: 0.025791
Sample size of AE DB......: 2423
Sample size of model......: 133
Missing data %............: 94.51094
- processing MCP1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.2443 -0.0164
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.0785 -0.6125 0.0000 0.5606 2.6138
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.319683 158.525085 -0.008 0.99337
currentDF[, TRAIT] -0.046677 0.087329 -0.534 0.59405
Age 0.000461 0.011024 0.042 0.96672
Gendermale 0.084068 0.191568 0.439 0.66161
ORdate_year 0.001938 0.078941 0.025 0.98046
Hypertension.compositeyes 0.148181 0.214320 0.691 0.49073
DiabetesStatusDiabetes 0.161569 0.206425 0.783 0.43544
SmokerStatusEx-smoker -0.025651 0.190008 -0.135 0.89285
SmokerStatusNever smoked 0.175029 0.323613 0.541 0.58967
Med.Statin.LLDyes -0.009641 0.191053 -0.050 0.95984
Med.all.antiplateletyes -0.253841 0.327746 -0.775 0.44025
GFR_MDRD -0.015294 0.005399 -2.833 0.00547 **
BMI 0.001164 0.021200 0.055 0.95632
MedHx_CVDyes 0.012324 0.184151 0.067 0.94676
stenose50-70% -1.407373 1.163649 -1.209 0.22902
stenose70-90% -1.289543 0.949761 -1.358 0.17725
stenose90-99% -1.534600 0.943590 -1.626 0.10666
stenose100% (Occlusion) -1.444954 1.340711 -1.078 0.28344
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9179 on 113 degrees of freedom
Multiple R-squared: 0.1478, Adjusted R-squared: 0.01959
F-statistic: 1.153 on 17 and 113 DF, p-value: 0.3149
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MCP1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MCP1_rank
Effect size...............: -0.046677
Standard error............: 0.087329
Odds ratio (effect size)..: 0.954
Lower 95% CI..............: 0.804
Upper 95% CI..............: 1.133
T-value...................: -0.534499
P-value...................: 0.5940466
R^2.......................: 0.1478
Adjusted r^2..............: 0.019593
Sample size of AE DB......: 2423
Sample size of model......: 131
Missing data %............: 94.59348
- processing MIP1a_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.18287 -0.01524
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.10781 -0.55655 -0.00523 0.61658 2.44444
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -53.957572 154.581327 -0.349 0.7278
currentDF[, TRAIT] 0.112198 0.099965 1.122 0.2644
Age 0.002664 0.012041 0.221 0.8253
Gendermale 0.050567 0.202904 0.249 0.8037
ORdate_year 0.028251 0.076983 0.367 0.7144
Hypertension.compositeyes 0.168769 0.224667 0.751 0.4543
DiabetesStatusDiabetes 0.293505 0.223586 1.313 0.1923
SmokerStatusEx-smoker -0.015914 0.205844 -0.077 0.9385
SmokerStatusNever smoked 0.129127 0.331703 0.389 0.6979
Med.Statin.LLDyes -0.156725 0.211173 -0.742 0.4597
Med.all.antiplateletyes -0.484615 0.395876 -1.224 0.2237
GFR_MDRD -0.013105 0.005583 -2.347 0.0209 *
BMI 0.002538 0.022220 0.114 0.9093
MedHx_CVDyes 0.051767 0.198392 0.261 0.7947
stenose50-70% -0.640887 1.353289 -0.474 0.6368
stenose70-90% -1.411733 0.960823 -1.469 0.1449
stenose90-99% -1.666240 0.963824 -1.729 0.0869 .
stenose100% (Occlusion) -1.181444 1.317013 -0.897 0.3718
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9267 on 101 degrees of freedom
Multiple R-squared: 0.1668, Adjusted R-squared: 0.02652
F-statistic: 1.189 on 17 and 101 DF, p-value: 0.287
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MIP1a_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MIP1a_rank
Effect size...............: 0.112198
Standard error............: 0.099965
Odds ratio (effect size)..: 1.119
Lower 95% CI..............: 0.92
Upper 95% CI..............: 1.361
T-value...................: 1.122373
P-value...................: 0.264365
R^2.......................: 0.166769
Adjusted r^2..............: 0.026522
Sample size of AE DB......: 2423
Sample size of model......: 119
Missing data %............: 95.08873
- processing RANTES_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.27555 -0.01674
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.1720 -0.6195 0.0000 0.6010 2.6151
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -50.618252 173.879543 -0.291 0.77150
currentDF[, TRAIT] 0.031550 0.095881 0.329 0.74272
Age 0.002146 0.011027 0.195 0.84606
Gendermale 0.072293 0.188477 0.384 0.70202
ORdate_year 0.026441 0.086612 0.305 0.76071
Hypertension.compositeyes 0.175559 0.212797 0.825 0.41109
DiabetesStatusDiabetes 0.160291 0.207105 0.774 0.44056
SmokerStatusEx-smoker -0.026335 0.188481 -0.140 0.88912
SmokerStatusNever smoked 0.162516 0.322530 0.504 0.61532
Med.Statin.LLDyes -0.026685 0.190233 -0.140 0.88869
Med.all.antiplateletyes -0.346046 0.313478 -1.104 0.27197
GFR_MDRD -0.014938 0.005400 -2.767 0.00661 **
BMI 0.003542 0.021071 0.168 0.86682
MedHx_CVDyes 0.017358 0.184129 0.094 0.92506
stenose50-70% -1.295858 1.165789 -1.112 0.26866
stenose70-90% -1.209357 0.943827 -1.281 0.20268
stenose90-99% -1.450278 0.941262 -1.541 0.12614
stenose100% (Occlusion) -1.307620 1.309002 -0.999 0.31994
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9179 on 114 degrees of freedom
Multiple R-squared: 0.1518, Adjusted R-squared: 0.02534
F-statistic: 1.2 on 17 and 114 DF, p-value: 0.2756
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' RANTES_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: RANTES_rank
Effect size...............: 0.03155
Standard error............: 0.095881
Odds ratio (effect size)..: 1.032
Lower 95% CI..............: 0.855
Upper 95% CI..............: 1.245
T-value...................: 0.329055
P-value...................: 0.7427179
R^2.......................: 0.151822
Adjusted r^2..............: 0.02534
Sample size of AE DB......: 2423
Sample size of model......: 132
Missing data %............: 94.55221
- processing MIG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.29106 -0.01703
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.16125 -0.56497 -0.01213 0.59805 2.57468
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -4.879612 156.818990 -0.031 0.97523
currentDF[, TRAIT] 0.121010 0.096872 1.249 0.21418
Age 0.001982 0.010940 0.181 0.85658
Gendermale 0.097814 0.187748 0.521 0.60340
ORdate_year 0.003691 0.078085 0.047 0.96239
Hypertension.compositeyes 0.159290 0.210559 0.757 0.45092
DiabetesStatusDiabetes 0.255104 0.210218 1.214 0.22746
SmokerStatusEx-smoker -0.002560 0.187858 -0.014 0.98915
SmokerStatusNever smoked 0.154519 0.321263 0.481 0.63147
Med.Statin.LLDyes -0.150572 0.193548 -0.778 0.43822
Med.all.antiplateletyes -0.274915 0.305850 -0.899 0.37064
GFR_MDRD -0.015301 0.005355 -2.857 0.00509 **
BMI 0.007003 0.020897 0.335 0.73816
MedHx_CVDyes 0.022540 0.182333 0.124 0.90184
stenose50-70% -1.516268 1.158118 -1.309 0.19311
stenose70-90% -1.433701 0.950548 -1.508 0.13427
stenose90-99% -1.650391 0.952762 -1.732 0.08596 .
stenose100% (Occlusion) -1.223823 1.297364 -0.943 0.34753
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9136 on 113 degrees of freedom
Multiple R-squared: 0.1647, Adjusted R-squared: 0.03904
F-statistic: 1.311 on 17 and 113 DF, p-value: 0.1986
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MIG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MIG_rank
Effect size...............: 0.12101
Standard error............: 0.096872
Odds ratio (effect size)..: 1.129
Lower 95% CI..............: 0.933
Upper 95% CI..............: 1.365
T-value...................: 1.249172
P-value...................: 0.2141836
R^2.......................: 0.164703
Adjusted r^2..............: 0.039039
Sample size of AE DB......: 2423
Sample size of model......: 131
Missing data %............: 94.59348
- processing IP10_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.28714 -0.01689
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.08206 -0.57042 -0.03433 0.58053 2.54662
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 13.137498 149.722455 0.088 0.93025
currentDF[, TRAIT] -0.021942 0.100584 -0.218 0.82774
Age -0.003050 0.011766 -0.259 0.79599
Gendermale 0.122518 0.190650 0.643 0.52188
ORdate_year -0.005172 0.074546 -0.069 0.94483
Hypertension.compositeyes 0.204422 0.209575 0.975 0.33162
DiabetesStatusDiabetes 0.281838 0.210173 1.341 0.18285
SmokerStatusEx-smoker 0.018368 0.195566 0.094 0.92535
SmokerStatusNever smoked 0.139415 0.365942 0.381 0.70400
Med.Statin.LLDyes -0.193192 0.200902 -0.962 0.33847
Med.all.antiplateletyes -0.283792 0.317269 -0.894 0.37313
GFR_MDRD -0.015868 0.005374 -2.953 0.00389 **
BMI 0.002942 0.020955 0.140 0.88862
MedHx_CVDyes 0.071493 0.188544 0.379 0.70533
stenose50-70% -1.474926 1.131665 -1.303 0.19534
stenose70-90% -1.309380 0.921243 -1.421 0.15822
stenose90-99% -1.474394 0.920248 -1.602 0.11215
stenose100% (Occlusion) -1.304966 1.274105 -1.024 0.30811
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8921 on 104 degrees of freedom
Multiple R-squared: 0.1763, Adjusted R-squared: 0.04169
F-statistic: 1.31 on 17 and 104 DF, p-value: 0.2012
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' IP10_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: IP10_rank
Effect size...............: -0.021942
Standard error............: 0.100584
Odds ratio (effect size)..: 0.978
Lower 95% CI..............: 0.803
Upper 95% CI..............: 1.191
T-value...................: -0.218145
P-value...................: 0.8277433
R^2.......................: 0.176326
Adjusted r^2..............: 0.041687
Sample size of AE DB......: 2423
Sample size of model......: 122
Missing data %............: 94.96492
- processing Eotaxin1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.3108 -0.0174
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.24663 -0.59020 0.01836 0.58650 2.62238
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -22.005475 150.853651 -0.146 0.88428
currentDF[, TRAIT] 0.132500 0.086157 1.538 0.12682
Age 0.001050 0.010929 0.096 0.92366
Gendermale 0.070113 0.189047 0.371 0.71141
ORdate_year 0.012240 0.075130 0.163 0.87087
Hypertension.compositeyes 0.126078 0.210523 0.599 0.55043
DiabetesStatusDiabetes 0.144952 0.206301 0.703 0.48371
SmokerStatusEx-smoker 0.018313 0.186983 0.098 0.92215
SmokerStatusNever smoked 0.174017 0.322046 0.540 0.59000
Med.Statin.LLDyes -0.060816 0.189430 -0.321 0.74876
Med.all.antiplateletyes -0.220758 0.300609 -0.734 0.46422
GFR_MDRD -0.016212 0.005348 -3.031 0.00301 **
BMI 0.005743 0.020973 0.274 0.78472
MedHx_CVDyes 0.095723 0.182796 0.524 0.60152
stenose50-70% -1.508593 1.164243 -1.296 0.19765
stenose70-90% -1.371645 0.948222 -1.447 0.15074
stenose90-99% -1.632256 0.948752 -1.720 0.08804 .
stenose100% (Occlusion) -1.230613 1.302806 -0.945 0.34685
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9175 on 115 degrees of freedom
Multiple R-squared: 0.167, Adjusted R-squared: 0.04386
F-statistic: 1.356 on 17 and 115 DF, p-value: 0.1718
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' Eotaxin1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: Eotaxin1_rank
Effect size...............: 0.1325
Standard error............: 0.086157
Odds ratio (effect size)..: 1.142
Lower 95% CI..............: 0.964
Upper 95% CI..............: 1.352
T-value...................: 1.537887
P-value...................: 0.1268232
R^2.......................: 0.166997
Adjusted r^2..............: 0.043858
Sample size of AE DB......: 2423
Sample size of model......: 133
Missing data %............: 94.51094
- processing TARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.29244 -0.01728
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.01077 -0.53628 -0.07451 0.52252 2.57954
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -3.348e+02 1.883e+02 -1.778 0.0786 .
currentDF[, TRAIT] 2.216e-01 1.096e-01 2.022 0.0460 *
Age 8.500e-03 1.158e-02 0.734 0.4649
Gendermale 1.338e-01 2.053e-01 0.652 0.5162
ORdate_year 1.683e-01 9.386e-02 1.793 0.0762 .
Hypertension.compositeyes 1.109e-01 2.370e-01 0.468 0.6409
DiabetesStatusDiabetes 3.255e-01 2.289e-01 1.422 0.1585
SmokerStatusEx-smoker -1.371e-01 1.982e-01 -0.692 0.4909
SmokerStatusNever smoked 2.003e-01 3.208e-01 0.624 0.5340
Med.Statin.LLDyes -2.819e-01 2.141e-01 -1.317 0.1912
Med.all.antiplateletyes -5.184e-01 3.171e-01 -1.635 0.1055
GFR_MDRD -1.357e-02 5.778e-03 -2.349 0.0209 *
BMI 8.264e-03 2.178e-02 0.379 0.7052
MedHx_CVDyes -3.923e-02 1.985e-01 -0.198 0.8437
stenose50-70% -4.633e-01 1.302e+00 -0.356 0.7228
stenose70-90% -1.514e+00 9.274e-01 -1.632 0.1059
stenose90-99% -1.806e+00 9.263e-01 -1.950 0.0542 .
stenose100% (Occlusion) -1.254e+00 1.272e+00 -0.986 0.3265
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.895 on 94 degrees of freedom
Multiple R-squared: 0.2243, Adjusted R-squared: 0.084
F-statistic: 1.599 on 17 and 94 DF, p-value: 0.07993
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' TARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: TARC_rank
Effect size...............: 0.221603
Standard error............: 0.1096
Odds ratio (effect size)..: 1.248
Lower 95% CI..............: 1.007
Upper 95% CI..............: 1.547
T-value...................: 2.021922
P-value...................: 0.04602685
R^2.......................: 0.224292
Adjusted r^2..............: 0.084005
Sample size of AE DB......: 2423
Sample size of model......: 112
Missing data %............: 95.37763
- processing PARC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.3108 -0.0174
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.0766 -0.5929 0.0000 0.5928 2.7296
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -12.219715 160.041389 -0.076 0.93927
currentDF[, TRAIT] -0.054175 0.093720 -0.578 0.56436
Age -0.000214 0.011018 -0.019 0.98454
Gendermale 0.111279 0.188684 0.590 0.55651
ORdate_year 0.007300 0.079690 0.092 0.92717
Hypertension.compositeyes 0.119400 0.212466 0.562 0.57523
DiabetesStatusDiabetes 0.159313 0.208031 0.766 0.44535
SmokerStatusEx-smoker 0.015460 0.188622 0.082 0.93482
SmokerStatusNever smoked 0.220050 0.324431 0.678 0.49897
Med.Statin.LLDyes -0.049103 0.191065 -0.257 0.79764
Med.all.antiplateletyes -0.195248 0.303256 -0.644 0.52096
GFR_MDRD -0.016559 0.005426 -3.052 0.00283 **
BMI 0.007329 0.021128 0.347 0.72929
MedHx_CVDyes 0.072757 0.183706 0.396 0.69280
stenose50-70% -1.446487 1.186418 -1.219 0.22526
stenose70-90% -1.272757 0.957191 -1.330 0.18626
stenose90-99% -1.477942 0.952394 -1.552 0.12345
stenose100% (Occlusion) -1.367703 1.326456 -1.031 0.30466
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9255 on 115 degrees of freedom
Multiple R-squared: 0.1523, Adjusted R-squared: 0.02702
F-statistic: 1.216 on 17 and 115 DF, p-value: 0.2636
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' PARC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: PARC_rank
Effect size...............: -0.054175
Standard error............: 0.09372
Odds ratio (effect size)..: 0.947
Lower 95% CI..............: 0.788
Upper 95% CI..............: 1.138
T-value...................: -0.578049
P-value...................: 0.5643615
R^2.......................: 0.152329
Adjusted r^2..............: 0.027021
Sample size of AE DB......: 2423
Sample size of model......: 133
Missing data %............: 94.51094
- processing MDC_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.18161 -0.01529
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.12789 -0.55897 -0.03942 0.61288 2.49192
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -1.048e+02 1.683e+02 -0.623 0.5349
currentDF[, TRAIT] 7.352e-02 1.017e-01 0.723 0.4714
Age 3.105e-03 1.148e-02 0.270 0.7874
Gendermale 9.349e-02 1.996e-01 0.468 0.6404
ORdate_year 5.353e-02 8.388e-02 0.638 0.5248
Hypertension.compositeyes 1.681e-01 2.239e-01 0.751 0.4546
DiabetesStatusDiabetes 3.106e-01 2.225e-01 1.396 0.1657
SmokerStatusEx-smoker -1.115e-02 2.035e-01 -0.055 0.9564
SmokerStatusNever smoked 1.589e-01 3.307e-01 0.481 0.6319
Med.Statin.LLDyes -1.512e-01 2.074e-01 -0.729 0.4677
Med.all.antiplateletyes -4.767e-01 3.957e-01 -1.205 0.2311
GFR_MDRD -1.291e-02 5.563e-03 -2.322 0.0222 *
BMI 2.869e-03 2.205e-02 0.130 0.8967
MedHx_CVDyes 2.948e-02 1.971e-01 0.150 0.8814
stenose50-70% -4.950e-01 1.347e+00 -0.367 0.7141
stenose70-90% -1.350e+00 9.582e-01 -1.408 0.1620
stenose90-99% -1.581e+00 9.609e-01 -1.645 0.1031
stenose100% (Occlusion) -1.214e+00 1.318e+00 -0.922 0.3588
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9274 on 102 degrees of freedom
Multiple R-squared: 0.16, Adjusted R-squared: 0.02002
F-statistic: 1.143 on 17 and 102 DF, p-value: 0.3253
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MDC_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MDC_rank
Effect size...............: 0.07352
Standard error............: 0.101703
Odds ratio (effect size)..: 1.076
Lower 95% CI..............: 0.882
Upper 95% CI..............: 1.314
T-value...................: 0.722883
P-value...................: 0.4714058
R^2.......................: 0.160016
Adjusted r^2..............: 0.020019
Sample size of AE DB......: 2423
Sample size of model......: 120
Missing data %............: 95.04746
- processing OPG_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.3108 -0.0174
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.0653 -0.6138 0.0000 0.5588 2.6819
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -41.474894 151.733084 -0.273 0.78508
currentDF[, TRAIT] -0.036324 0.089436 -0.406 0.68539
Age -0.000211 0.011038 -0.019 0.98478
Gendermale 0.120235 0.189788 0.634 0.52765
ORdate_year 0.021820 0.075574 0.289 0.77331
Hypertension.compositeyes 0.112682 0.214110 0.526 0.59971
DiabetesStatusDiabetes 0.152580 0.208345 0.732 0.46545
SmokerStatusEx-smoker 0.024301 0.190528 0.128 0.89873
SmokerStatusNever smoked 0.216920 0.324638 0.668 0.50535
Med.Statin.LLDyes -0.040460 0.193218 -0.209 0.83451
Med.all.antiplateletyes -0.167795 0.303639 -0.553 0.58160
GFR_MDRD -0.016279 0.005401 -3.014 0.00317 **
BMI 0.008167 0.021167 0.386 0.70035
MedHx_CVDyes 0.065790 0.183586 0.358 0.72073
stenose50-70% -1.319987 1.169432 -1.129 0.26136
stenose70-90% -1.199401 0.951309 -1.261 0.20994
stenose90-99% -1.397559 0.948631 -1.473 0.14342
stenose100% (Occlusion) -1.317869 1.321916 -0.997 0.32089
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9262 on 115 degrees of freedom
Multiple R-squared: 0.1511, Adjusted R-squared: 0.02559
F-statistic: 1.204 on 17 and 115 DF, p-value: 0.2726
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' OPG_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: OPG_rank
Effect size...............: -0.036324
Standard error............: 0.089436
Odds ratio (effect size)..: 0.964
Lower 95% CI..............: 0.809
Upper 95% CI..............: 1.149
T-value...................: -0.406143
P-value...................: 0.6853919
R^2.......................: 0.151083
Adjusted r^2..............: 0.025591
Sample size of AE DB......: 2423
Sample size of model......: 133
Missing data %............: 94.51094
- processing sICAM1_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.3108 -0.0174
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.1631 -0.5599 0.0000 0.5902 2.6879
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -7.052e+01 1.626e+02 -0.434 0.66532
currentDF[, TRAIT] 4.427e-02 9.040e-02 0.490 0.62528
Age 4.816e-04 1.104e-02 0.044 0.96528
Gendermale 1.110e-01 1.888e-01 0.588 0.55751
ORdate_year 3.630e-02 8.099e-02 0.448 0.65482
Hypertension.compositeyes 1.382e-01 2.146e-01 0.644 0.52083
DiabetesStatusDiabetes 1.455e-01 2.093e-01 0.695 0.48825
SmokerStatusEx-smoker 1.552e-02 1.887e-01 0.082 0.93459
SmokerStatusNever smoked 1.962e-01 3.254e-01 0.603 0.54770
Med.Statin.LLDyes -4.603e-02 1.915e-01 -0.240 0.81043
Med.all.antiplateletyes -1.801e-01 3.022e-01 -0.596 0.55242
GFR_MDRD -1.624e-02 5.397e-03 -3.009 0.00322 **
BMI 7.161e-03 2.115e-02 0.339 0.73559
MedHx_CVDyes 6.164e-02 1.838e-01 0.335 0.73798
stenose50-70% -1.299e+00 1.170e+00 -1.110 0.26932
stenose70-90% -1.175e+00 9.531e-01 -1.233 0.22020
stenose90-99% -1.401e+00 9.476e-01 -1.479 0.14200
stenose100% (Occlusion) -1.168e+00 1.329e+00 -0.879 0.38126
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9259 on 115 degrees of freedom
Multiple R-squared: 0.1516, Adjusted R-squared: 0.02622
F-statistic: 1.209 on 17 and 115 DF, p-value: 0.2686
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' sICAM1_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: sICAM1_rank
Effect size...............: 0.04427
Standard error............: 0.090403
Odds ratio (effect size)..: 1.045
Lower 95% CI..............: 0.876
Upper 95% CI..............: 1.248
T-value...................: 0.489698
P-value...................: 0.6252807
R^2.......................: 0.151635
Adjusted r^2..............: 0.026224
Sample size of AE DB......: 2423
Sample size of model......: 133
Missing data %............: 94.51094
- processing VEGFA_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + GFR_MDRD,
data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] GFR_MDRD
1.2647 0.2080 -0.0177
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.8403 -0.4509 0.0000 0.4621 2.6384
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 23.419451 174.943857 0.134 0.89380
currentDF[, TRAIT] 0.201711 0.105106 1.919 0.05803 .
Age 0.001453 0.010872 0.134 0.89399
Gendermale 0.206263 0.184521 1.118 0.26652
ORdate_year -0.010405 0.087130 -0.119 0.90520
Hypertension.compositeyes -0.116137 0.215111 -0.540 0.59056
DiabetesStatusDiabetes 0.228771 0.228607 1.001 0.31956
SmokerStatusEx-smoker 0.011947 0.186047 0.064 0.94894
SmokerStatusNever smoked 0.312174 0.318940 0.979 0.33023
Med.Statin.LLDyes -0.194011 0.186684 -1.039 0.30138
Med.all.antiplateletyes -0.193728 0.282931 -0.685 0.49522
GFR_MDRD -0.015446 0.005512 -2.802 0.00618 **
BMI -0.003146 0.022608 -0.139 0.88962
MedHx_CVDyes 0.147441 0.182151 0.809 0.42032
stenose50-70% -2.747210 1.272762 -2.158 0.03347 *
stenose70-90% -1.326175 0.872082 -1.521 0.13173
stenose90-99% -1.426869 0.867166 -1.645 0.10325
stenose100% (Occlusion) -1.104696 1.195239 -0.924 0.35775
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.8395 on 93 degrees of freedom
Multiple R-squared: 0.2427, Adjusted R-squared: 0.1042
F-statistic: 1.753 on 17 and 93 DF, p-value: 0.0467
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' VEGFA_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: VEGFA_rank
Effect size...............: 0.201711
Standard error............: 0.105106
Odds ratio (effect size)..: 1.223
Lower 95% CI..............: 0.996
Upper 95% CI..............: 1.503
T-value...................: 1.919127
P-value...................: 0.05803439
R^2.......................: 0.242657
Adjusted r^2..............: 0.104218
Sample size of AE DB......: 2423
Sample size of model......: 111
Missing data %............: 95.4189
- processing TGFB_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD
1.41396 -0.01849
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.23889 -0.56309 0.00781 0.59604 2.68961
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.802e+01 1.563e+02 -0.371 0.71116
currentDF[, TRAIT] 9.090e-02 9.466e-02 0.960 0.33896
Age 3.276e-05 1.097e-02 0.003 0.99762
Gendermale 1.356e-01 1.860e-01 0.729 0.46729
ORdate_year 3.020e-02 7.785e-02 0.388 0.69882
Hypertension.compositeyes 1.152e-01 2.111e-01 0.546 0.58641
DiabetesStatusDiabetes 2.219e-01 2.093e-01 1.060 0.29130
SmokerStatusEx-smoker 5.416e-02 1.915e-01 0.283 0.77787
SmokerStatusNever smoked 2.598e-01 3.127e-01 0.831 0.40774
Med.Statin.LLDyes -4.989e-02 1.955e-01 -0.255 0.79908
Med.all.antiplateletyes -1.524e-01 3.213e-01 -0.474 0.63620
GFR_MDRD -1.671e-02 5.316e-03 -3.143 0.00213 **
BMI -2.796e-03 2.203e-02 -0.127 0.89926
MedHx_CVDyes 1.208e-01 1.847e-01 0.654 0.51447
stenose50-70% -1.511e+00 1.181e+00 -1.279 0.20343
stenose70-90% -1.232e+00 9.514e-01 -1.295 0.19787
stenose90-99% -1.395e+00 9.476e-01 -1.472 0.14378
stenose100% (Occlusion) -1.298e+00 1.312e+00 -0.990 0.32429
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9238 on 113 degrees of freedom
Multiple R-squared: 0.1744, Adjusted R-squared: 0.05023
F-statistic: 1.404 on 17 and 113 DF, p-value: 0.1474
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' TGFB_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: TGFB_rank
Effect size...............: 0.0909
Standard error............: 0.094658
Odds ratio (effect size)..: 1.095
Lower 95% CI..............: 0.91
Upper 95% CI..............: 1.318
T-value...................: 0.960292
P-value...................: 0.3389586
R^2.......................: 0.174435
Adjusted r^2..............: 0.050235
Sample size of AE DB......: 2423
Sample size of model......: 131
Missing data %............: 94.59348
- processing MMP2_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + GFR_MDRD +
BMI, data = currentDF)
Coefficients:
(Intercept) currentDF[, TRAIT] GFR_MDRD BMI
0.27492 -0.15328 -0.01532 0.03403
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-1.9999 -0.5544 0.0000 0.5034 2.7511
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.454232 161.165396 0.177 0.86018
currentDF[, TRAIT] -0.149763 0.082695 -1.811 0.07282 .
Age -0.002187 0.011243 -0.194 0.84614
Gendermale 0.154033 0.193164 0.797 0.42690
ORdate_year -0.013234 0.080331 -0.165 0.86945
Hypertension.compositeyes 0.088188 0.215056 0.410 0.68254
DiabetesStatusDiabetes 0.131477 0.208788 0.630 0.53016
SmokerStatusEx-smoker 0.075844 0.182735 0.415 0.67890
SmokerStatusNever smoked 0.231602 0.317235 0.730 0.46687
Med.Statin.LLDyes -0.032684 0.185571 -0.176 0.86051
Med.all.antiplateletyes -0.176944 0.298748 -0.592 0.55485
GFR_MDRD -0.015672 0.005737 -2.732 0.00732 **
BMI 0.027723 0.022857 1.213 0.22773
MedHx_CVDyes 0.042060 0.184946 0.227 0.82051
stenose50-70% -1.335448 1.148900 -1.162 0.24756
stenose70-90% -1.338098 0.935830 -1.430 0.15554
stenose90-99% -1.509530 0.930541 -1.622 0.10757
stenose100% (Occlusion) -1.365175 1.289160 -1.059 0.29189
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.904 on 112 degrees of freedom
Multiple R-squared: 0.176, Adjusted R-squared: 0.05097
F-statistic: 1.408 on 17 and 112 DF, p-value: 0.1462
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MMP2_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MMP2_rank
Effect size...............: -0.149763
Standard error............: 0.082695
Odds ratio (effect size)..: 0.861
Lower 95% CI..............: 0.732
Upper 95% CI..............: 1.012
T-value...................: -1.811027
P-value...................: 0.07281704
R^2.......................: 0.176032
Adjusted r^2..............: 0.050965
Sample size of AE DB......: 2423
Sample size of model......: 130
Missing data %............: 94.63475
- processing MMP8_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD + BMI, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD BMI
0.36372 -0.01534 0.03010
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.0852 -0.5559 -0.0176 0.5795 2.7252
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -22.331472 161.028220 -0.139 0.8900
currentDF[, TRAIT] 0.040008 0.083853 0.477 0.6342
Age -0.003218 0.011395 -0.282 0.7781
Gendermale 0.190333 0.199395 0.955 0.3419
ORdate_year 0.012086 0.080264 0.151 0.8806
Hypertension.compositeyes 0.157863 0.214211 0.737 0.4627
DiabetesStatusDiabetes 0.136818 0.211593 0.647 0.5192
SmokerStatusEx-smoker 0.071535 0.185777 0.385 0.7009
SmokerStatusNever smoked 0.205115 0.321411 0.638 0.5247
Med.Statin.LLDyes -0.036323 0.188425 -0.193 0.8475
Med.all.antiplateletyes -0.224292 0.301589 -0.744 0.4586
GFR_MDRD -0.014862 0.005944 -2.500 0.0139 *
BMI 0.024408 0.023444 1.041 0.3000
MedHx_CVDyes 0.025986 0.187218 0.139 0.8899
stenose50-70% -1.275838 1.167208 -1.093 0.2767
stenose70-90% -1.237574 0.947403 -1.306 0.1941
stenose90-99% -1.374604 0.940238 -1.462 0.1465
stenose100% (Occlusion) -1.187487 1.302515 -0.912 0.3639
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9162 on 112 degrees of freedom
Multiple R-squared: 0.1536, Adjusted R-squared: 0.02516
F-statistic: 1.196 on 17 and 112 DF, p-value: 0.2795
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MMP8_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MMP8_rank
Effect size...............: 0.040008
Standard error............: 0.083853
Odds ratio (effect size)..: 1.041
Lower 95% CI..............: 0.883
Upper 95% CI..............: 1.227
T-value...................: 0.477117
P-value...................: 0.6342087
R^2.......................: 0.153623
Adjusted r^2..............: 0.025155
Sample size of AE DB......: 2423
Sample size of model......: 130
Missing data %............: 94.63475
- processing MMP9_rank
Call:
lm(formula = currentDF[, PROTEIN] ~ GFR_MDRD + BMI, data = currentDF)
Coefficients:
(Intercept) GFR_MDRD BMI
0.36372 -0.01534 0.03010
Call:
lm(formula = currentDF[, PROTEIN] ~ currentDF[, TRAIT] + Age +
Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF)
Residuals:
Min 1Q Median 3Q Max
-2.0569 -0.5931 -0.0049 0.6052 2.7163
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -8.724004 163.769977 -0.053 0.95761
currentDF[, TRAIT] -0.033284 0.081872 -0.407 0.68512
Age -0.003267 0.011399 -0.287 0.77494
Gendermale 0.221144 0.193553 1.143 0.25566
ORdate_year 0.005268 0.081641 0.065 0.94866
Hypertension.compositeyes 0.141059 0.219811 0.642 0.52236
DiabetesStatusDiabetes 0.146607 0.212662 0.689 0.49200
SmokerStatusEx-smoker 0.062595 0.185160 0.338 0.73595
SmokerStatusNever smoked 0.216336 0.321813 0.672 0.50281
Med.Statin.LLDyes -0.043787 0.188084 -0.233 0.81634
Med.all.antiplateletyes -0.218739 0.302063 -0.724 0.47048
GFR_MDRD -0.015595 0.005825 -2.677 0.00854 **
BMI 0.027336 0.023332 1.172 0.24384
MedHx_CVDyes 0.028606 0.187421 0.153 0.87896
stenose50-70% -1.273413 1.168578 -1.090 0.27818
stenose70-90% -1.222001 0.946437 -1.291 0.19931
stenose90-99% -1.351289 0.939181 -1.439 0.15300
stenose100% (Occlusion) -1.197217 1.304079 -0.918 0.36056
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9164 on 112 degrees of freedom
Multiple R-squared: 0.1532, Adjusted R-squared: 0.02461
F-statistic: 1.191 on 17 and 112 DF, p-value: 0.283
Analyzing in dataset ' AEDB.CEA ' the association of ' MCP1_plasma_olink_rank ' with ' MMP9_rank ' .
Collecting data.
We have collected the following and summarize it in an object:
Dataset...................: AEDB.CEA
Score/Exposure/biomarker..: MCP1_plasma_olink_rank
Trait/outcome.............: MMP9_rank
Effect size...............: -0.033284
Standard error............: 0.081872
Odds ratio (effect size)..: 0.967
Lower 95% CI..............: 0.824
Upper 95% CI..............: 1.136
T-value...................: -0.406537
P-value...................: 0.6851242
R^2.......................: 0.153152
Adjusted r^2..............: 0.024613
Sample size of AE DB......: 2423
Sample size of model......: 130
Missing data %............: 94.63475 cat("Edit the column names...\n")Edit the column names...colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
"Beta", "s.e.m.",
"OR", "low95CI", "up95CI",
"T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")
cat("Correct the variable types...\n")Correct the variable types...GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)
DT::datatable(GLM.results)
# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)
MCP1 levels vs. vulnerability index
Here we calculate the plaque instability/vulnerability index and visualize the MCP1 levels in plaque and plasma.
# Plaque vulnerability
table(AEDB.CEA$Macrophages.bin)
no/minor moderate/heavy
847 992 table(AEDB.CEA$Fat.bin_10)
<10% >10%
542 1316 table(AEDB.CEA$Collagen.bin)
no/minor moderate/heavy
382 1469 table(AEDB.CEA$SMC.bin)
no/minor moderate/heavy
602 1244 table(AEDB.CEA$IPH.bin)
no yes
746 1108 # SPSS code
#
# *** syntax- Plaque vulnerability**.
# COMPUTE Macro_instab = -999.
# IF macrophages.bin=2 Macro_instab=1.
# IF macrophages.bin=1 Macro_instab=0.
# EXECUTE.
#
# COMPUTE Fat10_instab = -999.
# IF Fat.bin_10=2 Fat10_instab=1.
# IF Fat.bin_10=1 Fat10_instab=0.
# EXECUTE.
#
# COMPUTE coll_instab=-999.
# IF Collagen.bin=2 coll_instab=0.
# IF Collagen.bin=1 coll_instab=1.
# EXECUTE.
#
#
# COMPUTE SMC_instab=-999.
# IF SMC.bin=2 SMC_instab=0.
# IF SMC.bin=1 SMC_instab=1.
# EXECUTE.
#
# COMPUTE IPH_instab=-999.
# IF IPH.bin=0 IPH_instab=0.
# IF IPH.bin=1 IPH_instab=1.
# EXECUTE.
#
# COMPUTE Instability=Macro_instab + Fat10_instab + coll_instab + SMC_instab + IPH_instab.
# EXECUTE.
# Fix plaquephenotypes
attach(AEDB.CEA)The following objects are masked from AEDB.CEA (pos = 3):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_ng_ml_2015,
Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q, AgeGroup, AgeGroupSex, AgeSQR, aid, AlcoholUse, Aldosteron_recode,
alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2,
AP_Dx, AP_Dx1, AP_Dx2, APOB, artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI,
BMI_US, BMI_WHO, BMI30ormore, BMIGroup, brain401, brain402, brain403, brain404, brain405, brain406, brain407, brain408, brain409,
brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history, CADPAOD_history, Calc.bin,
calcification, CalcificationPlaque, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS,
CEL, CFD_recalc, cholverl, cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, COL_Instability, collagen,
Collagen.bin, CollagenPlaque, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2, concablo3, concace2, concacei, concacet,
concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc, conccalc2,
conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort,
conccorthorm2, concderm, concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc,
concgluc2, concgluc3, concgluc4, concgrel, concinsu, conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr,
concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren, concresp, concrheu, concrheu2, concrheu3, concsta2,
concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos, cortihorm2, creat, crp_all,
CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst,
dateapprox1, dateapprox2, dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest,
dateexact_worst, dateexact1, dateexact2, dateexact3, dateexact4, dateok, dermacor, DiabetesStatus, diastoli, diet801, diet802,
diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813, diet814, diet815, diet816, diet817,
diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, eGFRGroup, EGR, EMMPRIN_45kD, EMMPRIN_58kD,
ENDOGLIN, endpoint1, endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, Eotaxin1_rank, EP_CAD, ep_cad_t_30days,
ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years,
EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_30days, ep_coronary_t_3years, ep_coronary_t_90days, EP_coronary_time,
EP_CVdeath, ep_cvdeath_t_30days, ep_cvdeath_t_3years, ep_cvdeath_t_90days, EP_CVdeath_time, EP_death, ep_death_t_30days,
ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke,
ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke,
ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time,
ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, ep_major_t_90days,
EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time, ep_nonstroke_t_3years,
EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days, ep_pta_t_3years,
EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, ep_stroke_t_90days, EP_stroke_time, EP_strokeCVdeath,
ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days,
ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking, epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years,
epcoronary.30days, epcoronary.3years, epcoronary.90days, epcvdeath.30days, epcvdeath.3years, epcvdeath.90days, epdeath.30days,
epdeath.3years, epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years,
epmajor.90days, epmi.30days, epmi.3years, epnonstroke.3years, epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years,
epstroke.30days, epstroke.3years, epstroke.90days, epstrokeCVdeath.30days, epstrokeCVdeath.3years, epstrokedeath.30days,
epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905,
exer906, exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4,
FABP4_pg_ug, FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40, FAT10_Instability, Fat10Perc, Femoral.interv, FH_AAA_broth,
FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp, FH_amp_mat, FH_amp_parent,
FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis,
FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis,
FH_CVD_broth, FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp,
FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat,
FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat, FH_HC_sibling,
FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat,
FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent,
FH_otherCVD_pat, FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat,
FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis,
FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp,
FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat,
FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent,
FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date,
FU.cutt.off.30days, FU.cutt.off.3years, FU.cutt.off.90days, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med,
Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb,
HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301,
heart302, heart303, heart304, heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314,
heart315, heart316, heart317, heart318, heart319, heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327,
heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON, Hypertension.composite, Hypertension.drugs,
Hypertension.selfreport, Hypertension.selfreportdrug, Hypertension1, Hypertension2, IL1_Beta, IL10, IL10_rank, IL12, IL12_rank, IL13,
IL13_rank, IL17, IL2, IL2_rank, IL21, IL21_rank, IL4, IL4_rank, IL5, IL5_rank, IL6, IL6_pg_ml_2015, IL6_pg_ug_2015, IL6_rank,
IL6R_pg_ml_2015, IL6R_pg_ug_2015, IL8, IL8_pg_ml_2015, IL8_pg_ug_2015, IL8_rank, IL9, IL9_rank, indexsymptoms_latest,
indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, INFG_rank, informedconsent, insulin, insuline, INVULDAT,
IP10, IP10_rank, IPH, IPH_extended.bin, IPH_Instability, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg,
LDL_clinic, LDL_dif, LDL_final, LDL_finalCU, ldl_source, LDL_var, LDLGroup, leg501, leg502, leg503, leg504, leg505, leg506, leg507,
leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516, leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R,
MAC_binned, MAC_grouped, MAC_Instability, macmean0, macrophages, Macrophages_LN, macrophages_location, Macrophages_rank,
Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1, MCP1_LN, MCP1_pg_ml_2015, MCP1_pg_ml_2015_LN, MCP1_pg_ml_2015_rank,
MCP1_pg_ug_2015, MCP1_pg_ug_2015_LN, MCP1_pg_ug_2015_rank, MCP1_plasma_olink, MCP1_plasma_olink_LN, MCP1_plasma_olink_rank,
MCP1_plasma_olink_rankNorm, MCP1_rank, MCSF_pg_ml_2015, MCSF_pg_ug_2015, MDC, MDC_rank, Med_notes, Med.ablock, Med.ACE_inh,
Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3, Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag,
Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag, Med.dipyridamole, Med.diuretic,
Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, MedHx_CVD, media,
MG_H1, MI_Dx, MI_Dx1, MI_Dx2, MIF, MIF_rank, MIG, MIG_rank, MIP1a, MIP1a_rank, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19,
miRNA155_RNU48, MMP14, MMP2, MMP2_rank, MMP2TIMP2, MMP8, MMP8_rank, MMP9, MMP9_rank, MMP9TIMP1, MPO_plasma, MRP_14, MRP_8, MRP_8_14C,
MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5,
neutrophils, NGAL, NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1,
NOD2, nogobt1_recalculated, NTproBNP_plasma, Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells,
Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706, oac707, oac708, oac709, oac710, oac711, oac712, oac713,
oac714, OKyear, OPG, OPG_plasma, OPG_rank, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2, oralgluc3,
oralgluc4, ORdate_epoch, ORdate_year, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ml_2015,
PAI1_pg_ug_2015, PAOD, PARC, PARC_rank, patch, PCSK9_plasma, PDGF_BB_plasma, Percentage_CD14, Percentage_CD20, Percentage_CD4,
Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, Plaque_Vulnerability_Index, plaquephenotype, PlateID_plasma_olink, positibl,
PrimaryLast, PrimaryLast1, prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306,
qual0307, qual0308, qual0309, qual0310, qual0401, qual0402, qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08,
qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909, qual1010, qual1101, qual1102, qual1103,
qual1104, RAAS_med, RANTES, RANTES_pg_ml_2015, RANTES_pg_ug_2015, RANTES_plasma, RANTES_rank, Ras, RE50_01, RE70_01, Renine_recode,
renineinh, restenos, restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608,
risk609, risk610, risk611, risk612, risk613, risk614, risk615, risk616, risk617, risk618, risk619, risk620, SBPGroup,
Segment_isolated_Tris_2015, SHBG, sICAM1, sICAM1_rank, SMAD1_5_8, SMAD2, SMAD3, smc, SMC_binned, SMC_grouped, SMC_Instability, SMC_LN,
smc_location, smc_macrophages_ratio, SMC_rank, SMC.bin, smcmean0, SmokerCurrent, SmokerStatus, SmokingReported, SmokingYearOR, stat3P,
statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral, Stenosis_ipsilateral, StenoticGroup,
Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER,
sympt, Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC,
TARC_rank, TAT_plasma, TC_2016, TC_all, TC_avg, TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all,
TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, TGFB_rank, thrombos, thrombus, thrombus_location, thrombus_new,
thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, TNFA_rank, totalchol, totalcholesterol_source, tractdig,
tractdig2, tractdig3, tractdig4, tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Tris_protein_conc_ug_ml_2015,
Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT, tropomaxpositief, TSratio_blood, TSratio_plaque,
UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001, VEGFA, VEGFA_plasma,
VEGFA_rank, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012,
vessel_density_Timo2012_2, vessel_density_Timo2013, VesselDensity_LN, VesselDensity_rank, vitamin, vitamin2, vitb12, VRAGENLIJST,
vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2, yearablo3, yearace, yearace2, yearacet,
yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc, yearcalc2,
yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort,
yearcorthorm2, yearderm, yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc,
yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu, yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr,
yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe, yearpros, yearpsy5, yearren, yearresp,
yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking, Yrs.smoking# mac instability
AEDB.CEA[,"MAC_Instability"] <- NA
AEDB.CEA$MAC_Instability[Macrophages.bin == -999] <- NA
AEDB.CEA$MAC_Instability[Macrophages.bin == "no/minor"] <- 0
AEDB.CEA$MAC_Instability[Macrophages.bin == "moderate/heavy"] <- 1
# fat instability
AEDB.CEA[,"FAT10_Instability"] <- NA
AEDB.CEA$FAT10_Instability[Fat.bin_10 == -999] <- NA
AEDB.CEA$FAT10_Instability[Fat.bin_10 == " <10%"] <- 0
AEDB.CEA$FAT10_Instability[Fat.bin_10 == " >10%"] <- 1
# col instability
AEDB.CEA[,"COL_Instability"] <- NA
AEDB.CEA$COL_Instability[Collagen.bin == -999] <- NA
AEDB.CEA$COL_Instability[Collagen.bin == "no/minor"] <- 1
AEDB.CEA$COL_Instability[Collagen.bin == "moderate/heavy"] <- 0
# smc instability
AEDB.CEA[,"SMC_Instability"] <- NA
AEDB.CEA$SMC_Instability[SMC.bin == -999] <- NA
AEDB.CEA$SMC_Instability[SMC.bin == "no/minor"] <- 1
AEDB.CEA$SMC_Instability[SMC.bin == "moderate/heavy"] <- 0
# iph instability
AEDB.CEA[,"IPH_Instability"] <- NA
AEDB.CEA$IPH_Instability[IPH.bin == -999] <- NA
AEDB.CEA$IPH_Instability[IPH.bin == "no"] <- 0
AEDB.CEA$IPH_Instability[IPH.bin == "yes"] <- 1
detach(AEDB.CEA)
table(AEDB.CEA$MAC_Instability, useNA = "ifany")
0 1 <NA>
847 992 584 table(AEDB.CEA$FAT10_Instability, useNA = "ifany")
0 1 <NA>
542 1316 565 table(AEDB.CEA$COL_Instability, useNA = "ifany")
0 1 <NA>
1469 382 572 table(AEDB.CEA$SMC_Instability, useNA = "ifany")
0 1 <NA>
1244 602 577 table(AEDB.CEA$IPH_Instability, useNA = "ifany")
0 1 <NA>
746 1108 569 # creating vulnerability index
AEDB.CEA <- AEDB.CEA %>% mutate(Plaque_Vulnerability_Index = factor(rowSums(.[grep("_Instability", names(.))], na.rm = TRUE)),
)
table(AEDB.CEA$Plaque_Vulnerability_Index, useNA = "ifany")
0 1 2 3 4 5
713 348 479 535 251 97 # str(AEDB.CEA$Plaque_Vulnerability_Index)
Here we plot the levels of inverse-rank normal transformed MCP1 plaque levels from experiment 1 and 2 to the Plaque vulnerability index.
library(sjlabelled)
attach(AEDB.CEA)The following objects are masked from AEDB.CEA (pos = 3):
ABI_70, ABI_max, ABI_mean, ABI_min, ABI_OP, ablock, ablock2, ablock3, aceinhib, aceinhib2, acetylsa, Adiponectin_ng_ml_2015,
Adiponectin_pg_ug_2015, AE_AAA_bijzonderheden, Age, Age_Q, AgeGroup, AgeGroupSex, AgeSQR, aid, AlcoholUse, Aldosteron_recode,
alg10201, alg10202, alg10203, alg10204, alg10205, alg105, alg106, alg109, alg110, alg113, alg114, alg115, ALOX5, analg2, analg3,
analgeti, Ang2, angioii, ANGPT2, anti_apoA1_IgG, anti_apoA1_index, anti_apoA1_na, antiall, antiall2, antiarrh, antiarrh2, ANXA2,
AP_Dx, AP_Dx1, AP_Dx2, APOB, artercon, Artery_summary, arteryop, AsymptSympt, AsymptSympt2G, bblock, bblock2, blocko, blocksnr, BMI,
BMI_US, BMI_WHO, BMI30ormore, BMIGroup, brain401, brain402, brain403, brain404, brain405, brain406, brain407, brain408, brain409,
brain410, brain411, brain412, brain413, brn40701, bspoed, CAD_Dx, CAD_Dx1, CAD_Dx2, CAD_history, CADPAOD_history, Calc.bin,
calcification, CalcificationPlaque, calcium, calcium2, calreg, carbasal, cardioembolic, Caspase3_7, CAV1, CD44, CD44V3, CEA_or_CAS,
CEL, CFD_recalc, cholverl, cholverl2, cholverl3, CI_history, clau1, clau2, Claudication, clopidog, CML, COL_Instability, collagen,
Collagen.bin, CollagenPlaque, combi1, combi2, combi3, comorbidity.DM, concablo, concablo2, concablo3, concace2, concacei, concacet,
concalle, concanal, concanal2, concanal3, concangi, concanta2, concanti, concanti2, concbblo, concbblo2, conccalc, conccalc2,
conccalreg, conccarb, concchol, concchol2, concchol3, concclau1, concclau2, concclop, conccom1, conccom2, conccom3, conccort,
conccorthorm2, concderm, concdig, concdig2, concdig3, concdig4, concdipy, concdiur, concdiur2, concdiur3, concerec, conceye, concgluc,
concgluc2, concgluc3, concgluc4, concgrel, concinsu, conciron, conciron2, concneur, concneur2, concneur3, concneur4, concnitr,
concnitr2, concotant, concotcor, concoth2, concothe, concpros, concpsy5, concren, concresp, concrheu, concrheu2, concrheu3, concsta2,
concstat, concthro, concthyr, concthyr2, concvit2, concvita, Contralateral_surgery, conwhen, corticos, cortihorm2, creat, crp_all,
CRP_avg, CRP_dif, crp_source, CRP_var, CST3_pg_ug, CST3_serum_luminex, CTGF, cTNI_plasma, CTSA, CTSB, CTSL1, CTSS, cyr61,
date_ic_patient, date_ic_researcher, Date.of.birth, date.previous.operation, date1yr, date3mon, dateapprox_latest, dateapprox_worst,
dateapprox1, dateapprox2, dateapprox3, dateapprox4, dateend1, dateend2, dateend3, dateend4, dateend5, dateend6, dateexact_latest,
dateexact_worst, dateexact1, dateexact2, dateexact3, dateexact4, dateok, dermacor, DiabetesStatus, diastoli, diet801, diet802,
diet803, diet804, diet805, diet806, diet807, diet808, diet809, diet810, diet811, diet812, diet813, diet814, diet815, diet816, diet817,
diet818, diet819, diet820, diet821, diet822, diet823, diet824, dipyridi, diuretic, diuretic2, diuretic3, DM, DM.composite,
duaalantiplatelet, duplend, eaindexl, eaindexr, eCigarettes, edaplaqu_recalc, edavrspl, eGFRGroup, EGR, EMMPRIN_45kD, EMMPRIN_58kD,
ENDOGLIN, endpoint1, endpoint2, endpoint3, endpoint4, endpoint5, endpoint6, Eotaxin1, Eotaxin1_rank, EP_CAD, ep_cad_t_30days,
ep_cad_t_3years, EP_CAD_time, ep_cad.30days, EP_CI, ep_ci_t_30days, ep_ci_t_3years, EP_CI_time, ep_com_t_30days, ep_com_t_3years,
EP_composite, EP_composite_time, EP_coronary, ep_coronary_t_30days, ep_coronary_t_3years, ep_coronary_t_90days, EP_coronary_time,
EP_CVdeath, ep_cvdeath_t_30days, ep_cvdeath_t_3years, ep_cvdeath_t_90days, EP_CVdeath_time, EP_death, ep_death_t_30days,
ep_death_t_3years, EP_death_time, EP_fatalCVA, ep_fatalCVA_t_30days, ep_fatalCVA_t_3years, EP_fatalCVA_time, EP_hemorrhagic_stroke,
ep_hemorrhagic_stroke_t_3years, EP_hemorrhagic_stroke_time, ep_hemorrhagic_stroke.3years, EP_ischemic_stroke,
ep_ischemic_stroke_t_3years, EP_ischemic_stroke_time, ep_ischemic_stroke.3years, EP_leg_amputation, EP_leg_amputation_time,
ep_legamputation_t_30days, ep_legamputation_t_3years, EP_major, ep_major_t_30days, ep_major_t_3years, ep_major_t_90days,
EP_major_time, EP_MI, ep_mi_t_30days, ep_mi_t_3years, EP_MI_time, EP_nonstroke_event, EP_nonstroke_event_time, ep_nonstroke_t_3years,
EP_peripheral, ep_peripheral_t_30days, ep_peripheral_t_3years, EP_peripheral_time, EP_pta, ep_pta_t_30days, ep_pta_t_3years,
EP_pta_time, EP_stroke, ep_stroke_t_30days, ep_stroke_t_3years, ep_stroke_t_90days, EP_stroke_time, EP_strokeCVdeath,
ep_strokeCVdeath_t_30days, ep_strokeCVdeath_t_3years, EP_strokeCVdeath_time, EP_strokedeath, ep_strokedeath_t_30days,
ep_strokedeath_t_3years, EP_strokedeath_time, ePackYearsSmoking, epcad.3years, epci.30days, epci.3years, epcom.30days, epcom.3years,
epcoronary.30days, epcoronary.3years, epcoronary.90days, epcvdeath.30days, epcvdeath.3years, epcvdeath.90days, epdeath.30days,
epdeath.3years, epfatalCVA.30days, epfatalCVA.3years, eplegamputation.30days, eplegamputation.3years, epmajor.30days, epmajor.3years,
epmajor.90days, epmi.30days, epmi.3years, epnonstroke.3years, epperipheral.30days, epperipheral.3years, eppta.30days, eppta.3years,
epstroke.30days, epstroke.3years, epstroke.90days, epstrokeCVdeath.30days, epstrokeCVdeath.3years, epstrokedeath.30days,
epstrokedeath.3years, erec, Estradiol, everstroke_composite, Everstroke_Ipsilateral, exer901, exer902, exer903, exer904, exer905,
exer906, exer9071, exer9072, exer9073, exer9074, exer9075, exer9076, exer908, exer909, exer910, eyedrop, EZis, FABP_serum, FABP4,
FABP4_pg_ug, FABP4_serum_luminex, fat, Fat.bin_10, Fat.bin_40, FAT10_Instability, Fat10Perc, Femoral.interv, FH_AAA_broth,
FH_AAA_comp, FH_AAA_mat, FH_AAA_parent, FH_AAA_pat, FH_AAA_sibling, FH_AAA_sis, FH_amp_broth, FH_amp_comp, FH_amp_mat, FH_amp_parent,
FH_amp_pat, FH_amp_sibling, FH_amp_sis, FH_CAD_broth, FH_CAD_comp, FH_CAD_mat, FH_CAD_parent, FH_CAD_pat, FH_CAD_sibling, FH_CAD_sis,
FH_corcalc_broth, FH_corcalc_comp, FH_corcalc_mat, FH_corcalc_parent, FH_corcalc_pat, FH_corcalc_sibling, FH_corcalc_sis,
FH_CVD_broth, FH_CVD_comp, FH_CVD_mat, FH_CVD_parent, FH_CVD_pat, FH_CVD_sibling, FH_CVD_sis, FH_CVdeath_broth, FH_CVdeath_comp,
FH_CVdeath_mat, FH_CVdeath_parent, FH_CVdeath_pat, FH_CVdeath_sibling, FH_CVdeath_sis, FH_DM_broth, FH_DM_comp, FH_DM_mat,
FH_DM_parent, FH_DM_pat, FH_DM_sibling, FH_DM_sis, FH_HC_broth, FH_HC_comp, FH_HC_mat, FH_HC_parent, FH_HC_pat, FH_HC_sibling,
FH_HC_sis, FH_HT_broth, FH_HT_comp, FH_HT_mat, FH_HT_parent, FH_HT_pat, FH_HT_sibling, FH_HT_sis, FH_MI_broth, FH_MI_comp, FH_MI_mat,
FH_MI_parent, FH_MI_pat, FH_MI_sibling, FH_MI_sis, FH_otherCVD_broth, FH_otherCVD_comp, FH_otherCVD_mat, FH_otherCVD_parent,
FH_otherCVD_pat, FH_otherCVD_sibling, FH_otherCVD_sis, FH_PAD_broth, FH_PAD_comp, FH_PAD_mat, FH_PAD_parent, FH_PAD_pat,
FH_PAD_sibling, FH_PAD_sis, FH_PAV_broth, FH_PAV_comp, FH_PAV_mat, FH_PAV_parent, FH_PAV_pat, FH_PAV_sibling, FH_PAV_sis,
FH_POB_broth, FH_POB_comp, FH_POB_mat, FH_POB_parent, FH_POB_pat, FH_POB_sibling, FH_POB_sis, FH_risk_broth, FH_risk_comp,
FH_risk_mat, FH_risk_parent, FH_risk_pat, FH_risk_sibling, FH_risk_sis, FH_Stroke_broth, FH_Stroke_comp, FH_Stroke_mat,
FH_Stroke_parent, FH_Stroke_pat, FH_Stroke_sibling, FH_Stroke_sis, FH_tromb_broth, FH_tromb_comp, FH_tromb_mat, FH_tromb_parent,
FH_tromb_pat, FH_tromb_sibling, FH_tromb_sis, filter_$, folicaci, followup1, followup2, followup3, Fontaine, FU_check, FU_check_date,
FU.cutt.off.30days, FU.cutt.off.3years, FU.cutt.off.90days, FU1JAAR, FU2JAAR, FU3JAAR, FURIN_low, FURIN_up, GDF15_plasma, geen_med,
Gender, GFR_CG, GFR_MDRD, glucose, GR_Segment, GrB_plaque, GrB_serum, grel, GrK_plaque, GrK_serum, GrM_plaque, GrM_serum, HA, hb,
HDAC9, HDL, HDL_2016, HDL_all, HDL_avg, HDL_clinic, HDL_dif, HDL_final, HDL_finalCU, hdl_source, HDL_var, heart300, heart301,
heart302, heart303, heart304, heart305, heart306, heart307, heart308, heart309, heart310, heart311, heart312, heart313, heart314,
heart315, heart316, heart317, heart318, heart319, heart320, heart321, heart322, heart323, heart324, heart325, heart326, heart327,
heart328, HIF1A, ho1, homocys, Hospital, hrt31301, hsCRP_plasma, ht, HYAL55KD, HYALURON, Hypertension.composite, Hypertension.drugs,
Hypertension.selfreport, Hypertension.selfreportdrug, Hypertension1, Hypertension2, IL1_Beta, IL10, IL10_rank, IL12, IL12_rank, IL13,
IL13_rank, IL17, IL2, IL2_rank, IL21, IL21_rank, IL4, IL4_rank, IL5, IL5_rank, IL6, IL6_pg_ml_2015, IL6_pg_ug_2015, IL6_rank,
IL6R_pg_ml_2015, IL6R_pg_ug_2015, IL8, IL8_pg_ml_2015, IL8_pg_ug_2015, IL8_rank, IL9, IL9_rank, indexsymptoms_latest,
indexsymptoms_latest_4g, indexsymptoms_worst, indexsymptoms_worst_4g, INFG, INFG_rank, informedconsent, insulin, insuline, INVULDAT,
IP10, IP10_rank, IPH, IPH_extended.bin, IPH_Instability, IPH.bin, ironfoli, ironfoli2, KDOQI, latest, LDL, LDL_2016, LDL_all, LDL_avg,
LDL_clinic, LDL_dif, LDL_final, LDL_finalCU, ldl_source, LDL_var, LDLGroup, leg501, leg502, leg503, leg504, leg505, leg506, leg507,
leg508, leg509, leg510, leg511, leg512, leg513, leg514, leg515, leg516, leg517, leg518, leg519, leg520, LMW1STME, LTB4, LTB4R,
MAC_binned, MAC_grouped, MAC_Instability, macmean0, macrophages, Macrophages_LN, macrophages_location, Macrophages_rank,
Macrophages.bin, MAP, Mast_cells_plaque, max.followup, MCP1, MCP1_LN, MCP1_pg_ml_2015, MCP1_pg_ml_2015_LN, MCP1_pg_ml_2015_rank,
MCP1_pg_ug_2015, MCP1_pg_ug_2015_LN, MCP1_pg_ug_2015_rank, MCP1_plasma_olink, MCP1_plasma_olink_LN, MCP1_plasma_olink_rank,
MCP1_plasma_olink_rankNorm, MCP1_rank, MCSF_pg_ml_2015, MCSF_pg_ug_2015, MDC, MDC_rank, Med_notes, Med.ablock, Med.ACE_inh,
Med.acetylsal, Med.acetylsal_Combi1, Med.acetylsal_Combi2, Med.acetylsal_Combi3, Med.ADPinh, Med.all.antiplatelet, Med.angiot2.antag,
Med.antiarrh, Med.anticoagulants, Med.ascal, Med.aspirin.derived, Med.bblocker, Med.calc_antag, Med.dipyridamole, Med.diuretic,
Med.LLD, Med.nitrate, Med.otheranthyp, Med.renin, Med.statin, Med.statin.derived, Med.Statin.LLD, Med.statin2, MedHx_CVD, media,
MG_H1, MI_Dx, MI_Dx1, MI_Dx2, MIF, MIF_rank, MIG, MIG_rank, MIP1a, MIP1a_rank, miRNA100_RNU19, miRNA100_RNU48, miRNA155_RNU19,
miRNA155_RNU48, MMP14, MMP2, MMP2_rank, MMP2TIMP2, MMP8, MMP8_rank, MMP9, MMP9_rank, MMP9TIMP1, MPO_plasma, MRP_14, MRP_8, MRP_8_14C,
MRP_8_14C_buhlmann, MRP14_plasma, MRP8_14C_plasma, MRP8_plasma, negatibl, neuropsy, neuropsy2, neuropsy3, neuropsy4, neurpsy5,
neutrophils, NGAL, NGAL_low, NGAL_MMP9_complex, NGAL_MMP9_local, NGAL_MMP9_peripheral, NGAL_total, NGAL_up, nitrate, nitrate2, NOD1,
NOD2, nogobt1_recalculated, NTproBNP_plasma, Number_Events_Sorter, Number_Sorted_CD14, Number_Sorted_CD20, Number_Sorted_CD4_Cells,
Number_Sorted_CD8_Cells, oac701, oac702, oac70305, oac704, oac705, oac706, oac707, oac708, oac709, oac710, oac711, oac712, oac713,
oac714, OKyear, OPG, OPG_plasma, OPG_rank, OPN, OPN_2013, OPN_plasma, OR_blood, Oral.glucose.inh, oralgluc, oralgluc2, oralgluc3,
oralgluc4, ORdate_epoch, ORdate_year, ORyear, othanthyp, othcoron, other, other2, OverallPlaquePhenotype, PAI1_pg_ml_2015,
PAI1_pg_ug_2015, PAOD, PARC, PARC_rank, patch, PCSK9_plasma, PDGF_BB_plasma, Percentage_CD14, Percentage_CD20, Percentage_CD4,
Percentage_CD8, Peripheral.interv, PKC, PLA2_plasma, Plaque_Vulnerability_Index, plaquephenotype, PlateID_plasma_olink, positibl,
PrimaryLast, PrimaryLast1, prostagl, PulsePressure, qual01, qual02, qual0301, qual0302, qual0303, qual0304, qual0305, qual0306,
qual0307, qual0308, qual0309, qual0310, qual0401, qual0402, qual0403, qual0404, qual0501, qual0502, qual0503, qual06, qual07, qual08,
qual0901, qual0902, qual0903, qual0904, qual0905, qual0906, qual0907, qual0908, qual0909, qual1010, qual1101, qual1102, qual1103,
qual1104, RAAS_med, RANTES, RANTES_pg_ml_2015, RANTES_pg_ug_2015, RANTES_plasma, RANTES_rank, Ras, RE50_01, RE70_01, Renine_recode,
renineinh, restenos, restenosisOK, rheuma, rheuma2, rheuma3, risk601, risk602, risk603, risk604, risk605, risk606, risk607, risk608,
risk609, risk610, risk611, risk612, risk613, risk614, risk615, risk616, risk617, risk618, risk619, risk620, SBPGroup,
Segment_isolated_Tris_2015, SHBG, sICAM1, sICAM1_rank, SMAD1_5_8, SMAD2, SMAD3, smc, SMC_binned, SMC_grouped, SMC_Instability, SMC_LN,
smc_location, smc_macrophages_ratio, SMC_rank, SMC.bin, smcmean0, SmokerCurrent, SmokerStatus, SmokingReported, SmokingYearOR, stat3P,
statin2, statines, ste3mext, sten1yr, sten3mo, stenose, stenosis_con_bin, Stenosis_contralateral, Stenosis_ipsilateral, StenoticGroup,
Stroke_Dx, Stroke_eitherside, Stroke_history, Stroke_Symptoms, StrokeTIA_Dx, StrokeTIA_history, StrokeTIA_Symptoms, STUDY_NUMBER,
sympt, Sympt_latest, Sympt_worst, sympt1, sympt2, sympt3, sympt4, Symptoms.3g, Symptoms.4g, Symptoms.5G, systolic, T_NUMBER, TARC,
TARC_rank, TAT_plasma, TC_2016, TC_all, TC_avg, TC_clinic, TC_dif, TC_final, TC_finalCU, TC_var, Testosterone, TG_2016, TG_all,
TG_avg, TG_clinic, TG_dif, TG_final, TG_finalCU, TG_var, TGF, TGFB, TGFB_rank, thrombos, thrombus, thrombus_location, thrombus_new,
thrombus_organization, thrombus_organization_v2, thrombus_percentage, thyros2, thyrosta, Time_event_OR, TimeOR_latest,
TimeOR_latest_4g, TimeOR_worst, TimeOR_worst_4g, TIMP1, TIMP2, TISNOW, TNFA, TNFA_rank, totalchol, totalcholesterol_source, tractdig,
tractdig2, tractdig3, tractdig4, tractres, Treatment.DM, TREM1, triglyceride_source, triglyceriden, Tris_protein_conc_ug_ml_2015,
Trop1, Trop1DT, Trop2, Trop2DT, Trop3, Trop3DT, TropmaxpostOK, TropoMax, TropoMaxDT, tropomaxpositief, TSratio_blood, TSratio_plaque,
UPID, validation_date, validation1, validation2, validation3, validation4, validation5, validation6, VAR00001, VEGFA, VEGFA_plasma,
VEGFA_rank, vegfa422, vessel_density, vessel_density_additional, vessel_density_averaged, vessel_density_Timo2012,
vessel_density_Timo2012_2, vessel_density_Timo2013, VesselDensity_LN, VesselDensity_rank, vitamin, vitamin2, vitb12, VRAGENLIJST,
vWF_plasma, WBC_THAW, Which.femoral.artery, Whichoperation, writtenIC, yearablo, yearablo2, yearablo3, yearace, yearace2, yearacet,
yearanal, yearanal2, yearanal3, yearangi, yearanta, yearanta2, yearanti, yearanti2, yearbblo, yearbblo2, yearcalc, yearcalc2,
yearcalreg, yearcarb, yearchol, yearchol2, yearchol3, yearclau1, yearclau2, yearclop, yearcom1, yearcom2, yearcom3, yearcort,
yearcorthorm2, yearderm, yeardig, yeardig2, yeardig3, yeardig4, yeardipy, yeardiur, yeardiur2, yeardiur3, yearerec, yeareye, yeargluc,
yeargluc2, yeargluc3, yeargluc4, yeargrel, yearinsu, yeariron, yeariron2, yearneur, yearneur2, yearneur3, yearneur4, yearnitr,
yearnitr2, yearOR_bin_2010, YearOR_per2years, yearotant, yearotcor, yearoth2, yearothe, yearpros, yearpsy5, yearren, yearresp,
yearrheu, yearrheu2, yearrheu3, yearsta2, yearstat, yearthro, yearthyr, yearthyr2, yearvit2, yearvita, Yrs.no.smoking, Yrs.smokingAEDB.CEA$yeartemp <- as.numeric(year(AEDB.CEA$dateok))
AEDB.CEA[,"ORyearGroup"] <- NA
AEDB.CEA$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AEDB.CEA$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AEDB.CEA)
table(AEDB.CEA$ORyearGroup, AEDB.CEA$ORdate_year)
2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
< 2007 81 157 190 185 183 152 0 0 0 0 0 0 0 0 0 0 0 0
> 2007 0 0 0 0 0 0 138 182 159 164 176 149 163 76 85 65 66 52MCP1 plaque levels vs. plaque vulnerability index
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_pg_ug_2015_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/ug]\n(INT, exp 2)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgug.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_pg_ml_2015_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgmL.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p3 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p3, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp1_pgmL.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_pg_ug_2015_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/ug]\n(INT, exp 2)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgug.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_pg_ml_2015_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgmL.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup, data = AEDB.CEA, method = "kruskal.test")
p3 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") #+
# stat_compare_means(method = "kruskal.test")
ggpar(p3, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp1_pgmL.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
MCP1 plasma levels vs. plaque vulnerability index
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender, data = AEDB.CEA, method = "wilcox.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_plasma_olink_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plasma [AU]\n(inverse-normal transformation)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plasma.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_plasma_olink_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plasma [AU]\n(inverse-normal transformation)",
color = "Plaque_Vulnerability_Index",
palette = "npg",
facet.by = "ORyearGroup",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plasma.PlaqueVulnerabilityIndex_FacetbyYear.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
p1 <- ggpubr::ggboxplot(AEDB.CEA,
x = "Plaque_Vulnerability_Index",
y = "MCP1_plasma_olink_rank",
xlab = "Plaque vulnerability index",
ylab = "MCP1 plasma [AU]\n(inverse-normal transformation)",
color = "ORyearGroup",
palette = "npg",
add = "jitter") #+
# stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plasma.PlaqueVulnerabilityIndex_byYear.pdf"), plot = last_plot())Saving 7.29 x 4.51 in image
rm(p1)
Model 1
In this model we correct for Age, Gender, and year of surgery.
Here we use the inverse-rank normalized data - visually this is more normally distributed.
Analysis of the plaque vulnerability indez as a function of plasma/plaque MCP1 levels.
TRAITS.PROTEIN.RANK.extra = c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank", "MCP1_rank", "MCP1_plasma_olink_rank")
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK.extra)) {
PROTEIN = TRAITS.PROTEIN.RANK.extra[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "Plaque_Vulnerability_Index"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1, ORdate_epoch) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
droplevels(.)
# fix numeric OR year
currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
# table(currentDF$ORdate_year)
### univariate
# + Hypertension.composite + DiabetesStatus + SmokerCurrent +
# Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI +
# CAD_history + Stroke_history + Peripheral.interv + stenose
fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
data = currentDF,
Hess = TRUE)
print(summary(fit))
## store table
(ctable <- coef(summary(fit)))
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
print((ctable <- cbind(ctable, "p value" = p)))
}
Analysis of MCP1_pg_ug_2015_rank.
- processing Plaque_Vulnerability_Index
Call:
polr(formula = currentDF[, TRAIT] ~ currentDF[, PROTEIN] + Age +
Gender + ORdate_year, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.22974 0.0520658 4.412
Age 0.01146 0.0064644 1.773
Gendermale 0.75526 0.1139703 6.627
ORdate_year -0.16621 0.0002403 -691.760
Intercepts:
Value Std. Error t value
0|1 -334.7597 0.0043 -78282.6858
1|2 -333.3455 0.0922 -3613.6521
2|3 -332.1538 0.1055 -3148.5999
3|4 -330.6113 0.1233 -2682.2176
4|5 -328.8917 0.1811 -1816.2604
Residual Deviance: 3775.709
AIC: 3793.709
Value Std. Error t value p value
currentDF[, PROTEIN] 0.22973882 0.0520657989 4.412471 1.021976e-05
Age 0.01146118 0.0064644492 1.772956 7.623601e-02
Gendermale 0.75525514 0.1139703340 6.626770 3.431127e-11
ORdate_year -0.16620537 0.0002402644 -691.760307 0.000000e+00
0|1 -334.75966137 0.0042762925 -78282.685826 0.000000e+00
1|2 -333.34553517 0.0922461607 -3613.652131 0.000000e+00
2|3 -332.15375667 0.1054925271 -3148.599865 0.000000e+00
3|4 -330.61131055 0.1232604354 -2682.217610 0.000000e+00
4|5 -328.89167468 0.1810817842 -1816.260405 0.000000e+00
Analysis of MCP1_pg_ml_2015_rank.
- processing Plaque_Vulnerability_Index
Call:
polr(formula = currentDF[, TRAIT] ~ currentDF[, PROTEIN] + Age +
Gender + ORdate_year, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.41240 0.0532549 7.744
Age 0.01012 0.0064653 1.565
Gendermale 0.64970 0.1152482 5.637
ORdate_year -0.19973 0.0002414 -827.253
Intercepts:
Value Std. Error t value
0|1 -402.2399 0.0041 -99035.6421
1|2 -400.8099 0.0927 -4325.4754
2|3 -399.5923 0.1060 -3770.4681
3|4 -398.0243 0.1241 -3208.1032
4|5 -396.2918 0.1820 -2177.0531
Residual Deviance: 3747.495
AIC: 3765.495
Value Std. Error t value p value
currentDF[, PROTEIN] 0.41239585 0.053254882 7.743813 9.647894e-15
Age 0.01011714 0.006465267 1.564844 1.176194e-01
Gendermale 0.64969711 0.115248205 5.637373 1.726639e-08
ORdate_year -0.19972708 0.000241434 -827.253409 0.000000e+00
0|1 -402.23988620 0.004061567 -99035.642101 0.000000e+00
1|2 -400.80990883 0.092662627 -4325.475353 0.000000e+00
2|3 -399.59231347 0.105979499 -3770.468053 0.000000e+00
3|4 -398.02430118 0.124068422 -3208.103198 0.000000e+00
4|5 -396.29184933 0.182031321 -2177.053081 0.000000e+00
Analysis of MCP1_rank.
- processing Plaque_Vulnerability_Index
Call:
polr(formula = currentDF[, TRAIT] ~ currentDF[, PROTEIN] + Age +
Gender + ORdate_year, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.57914 0.081983 7.064
Age 0.01674 0.010018 1.671
Gendermale 0.67004 0.173922 3.853
ORdate_year 0.11245 0.000364 308.898
Intercepts:
Value Std. Error t value
0|1 223.5627 0.0048 46996.7054
1|2 225.2892 0.1979 1138.6231
2|3 226.6480 0.2156 1051.2929
3|4 228.3111 0.2372 962.6942
4|5 229.9356 0.2885 796.9079
Residual Deviance: 1682.273
AIC: 1700.273
Value Std. Error t value p value
currentDF[, PROTEIN] 0.57914246 0.0819826492 7.064208 1.615350e-12
Age 0.01674476 0.0100184222 1.671396 9.464339e-02
Gendermale 0.67004219 0.1739220743 3.852543 1.168977e-04
ORdate_year 0.11245196 0.0003640419 308.898413 0.000000e+00
0|1 223.56270901 0.0047569868 46996.705365 0.000000e+00
1|2 225.28918129 0.1978610688 1138.623088 0.000000e+00
2|3 226.64797639 0.2155897515 1051.292906 0.000000e+00
3|4 228.31109565 0.2371584838 962.694195 0.000000e+00
4|5 229.93564181 0.2885347907 796.907857 0.000000e+00
Analysis of MCP1_plasma_olink_rank.
- processing Plaque_Vulnerability_Index
Call:
polr(formula = currentDF[, TRAIT] ~ currentDF[, PROTEIN] + Age +
Gender + ORdate_year, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] -0.13612 0.0731441 -1.861
Age 0.01139 0.0098763 1.153
Gendermale 0.54853 0.1492376 3.676
ORdate_year -0.25718 0.0003811 -674.757
Intercepts:
Value Std. Error t value
0|1 -516.8352 0.0050 -103656.5302
1|2 -515.8462 0.0834 -6185.9742
2|3 -514.9276 0.1031 -4994.2955
3|4 -513.5048 0.1360 -3777.0617
4|5 -512.2499 0.1933 -2649.5723
Residual Deviance: 2133.562
AIC: 2151.562
Value Std. Error t value p value
currentDF[, PROTEIN] -0.13612369 0.0731441107 -1.861034e+00 0.062739345
Age 0.01139166 0.0098762592 1.153439e+00 0.248730239
Gendermale 0.54852544 0.1492376173 3.675517e+00 0.000237368
ORdate_year -0.25718065 0.0003811456 -6.747570e+02 0.000000000
0|1 -516.83518562 0.0049860359 -1.036565e+05 0.000000000
1|2 -515.84621578 0.0833896494 -6.185974e+03 0.000000000
2|3 -514.92764397 0.1031031584 -4.994296e+03 0.000000000
3|4 -513.50478478 0.1359535054 -3.777062e+03 0.000000000
4|5 -512.24989652 0.1933330540 -2.649572e+03 0.000000000Model 2
In this model we correct for Age, Gender, Hypertension status, Diabetes status, current smoker status, lipid-lowering drugs (LLDs), antiplatelet medication, eGFR (MDRD), BMI, MedHx_CVD (combination of CAD history, stroke history, and peripheral interventions), and stenosis..
for (protein in 1:length(TRAITS.PROTEIN.RANK.extra)) {
PROTEIN = TRAITS.PROTEIN.RANK.extra[protein]
cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
TRAIT = "Plaque_Vulnerability_Index"
cat(paste0("\n- processing ",TRAIT,"\n\n"))
currentDF <- as.data.frame(AEDB.CEA %>%
dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
filter(complete.cases(.))) %>%
filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
droplevels(.)
# fix numeric OR year
currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
# for debug
# print(DT::datatable(currentDF))
# print(nrow(currentDF))
# print(str(currentDF))
# print(class(currentDF[,TRAIT]))
### univariate
fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
data = currentDF,
Hess = TRUE)
print(summary(fit))
## store table
(ctable <- coef(summary(fit)))
## calculate and store p values
p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
## combined table
print((ctable <- cbind(ctable, "p value" = p)))
}
Analysis of MCP1_pg_ug_2015_rank.
- processing Plaque_Vulnerability_Index
Call:
polr(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.228047 0.056478 4.0378
Age 0.007808 0.012084 0.6462
Gendermale 0.760609 0.127182 5.9805
ORdate_year -0.177792 0.000823 -216.0297
Hypertension.compositeyes -0.126849 0.174560 -0.7267
DiabetesStatusDiabetes -0.144444 0.137568 -1.0500
SmokerStatusEx-smoker 0.085838 0.129332 0.6637
SmokerStatusNever smoked 0.543817 0.184715 2.9441
Med.Statin.LLDyes 0.070612 0.144823 0.4876
Med.all.antiplateletyes -0.053570 0.206360 -0.2596
GFR_MDRD -0.002325 0.004066 -0.5718
BMI -0.003801 0.023201 -0.1638
MedHx_CVDyes 0.147107 0.117907 1.2477
stenose50-70% -0.787612 0.157870 -4.9890
stenose70-90% -0.720064 0.095073 -7.5738
stenose90-99% -0.886088 0.097784 -9.0617
stenose100% (Occlusion) -1.649069 0.011362 -145.1390
stenose50-99% -1.449354 0.006847 -211.6876
stenose70-99% -1.316786 0.008036 -163.8595
Intercepts:
Value Std. Error t value
0|1 -359.3301 0.0619 -5800.4612
1|2 -357.8203 0.1370 -2612.1494
2|3 -356.6089 0.1542 -2313.2703
3|4 -355.0500 0.1494 -2376.9984
4|5 -353.3730 0.1966 -1797.4815
Residual Deviance: 3246.932
AIC: 3294.932
Value Std. Error t value p value
currentDF[, PROTEIN] 2.280469e-01 0.0564780166 4.0377997 5.395489e-05
Age 7.808445e-03 0.0120841895 0.6461703 5.181690e-01
Gendermale 7.606088e-01 0.1271815191 5.9804977 2.224569e-09
ORdate_year -1.777916e-01 0.0008229957 -216.0297379 0.000000e+00
Hypertension.compositeyes -1.268491e-01 0.1745595028 -0.7266809 4.674214e-01
DiabetesStatusDiabetes -1.444443e-01 0.1375683773 -1.0499820 2.937264e-01
SmokerStatusEx-smoker 8.583754e-02 0.1293318029 0.6637002 5.068822e-01
SmokerStatusNever smoked 5.438166e-01 0.1847151528 2.9440823 3.239138e-03
Med.Statin.LLDyes 7.061192e-02 0.1448234604 0.4875724 6.258528e-01
Med.all.antiplateletyes -5.357011e-02 0.2063596905 -0.2595958 7.951756e-01
GFR_MDRD -2.325120e-03 0.0040663392 -0.5717968 5.674596e-01
BMI -3.800757e-03 0.0232006280 -0.1638213 8.698718e-01
MedHx_CVDyes 1.471071e-01 0.1179066463 1.2476574 2.121565e-01
stenose50-70% -7.876117e-01 0.1578698915 -4.9889922 6.069509e-07
stenose70-90% -7.200641e-01 0.0950734118 -7.5737700 3.625462e-14
stenose90-99% -8.860878e-01 0.0977839248 -9.0616916 1.284429e-19
stenose100% (Occlusion) -1.649069e+00 0.0113619997 -145.1389659 0.000000e+00
stenose50-99% -1.449354e+00 0.0068466639 -211.6875611 0.000000e+00
stenose70-99% -1.316786e+00 0.0080360663 -163.8594739 0.000000e+00
0|1 -3.593301e+02 0.0619485421 -5800.4611789 0.000000e+00
1|2 -3.578203e+02 0.1369830873 -2612.1493858 0.000000e+00
2|3 -3.566089e+02 0.1541578905 -2313.2702894 0.000000e+00
3|4 -3.550500e+02 0.1493690323 -2376.9983963 0.000000e+00
4|5 -3.533730e+02 0.1965934232 -1797.4814740 0.000000e+00
Analysis of MCP1_pg_ml_2015_rank.
- processing Plaque_Vulnerability_Index
Call:
polr(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.418370 0.0579871 7.2149
Age 0.005896 0.0120649 0.4887
Gendermale 0.662747 0.1286571 5.1513
ORdate_year -0.211763 0.0008317 -254.6074
Hypertension.compositeyes -0.131494 0.1741966 -0.7549
DiabetesStatusDiabetes -0.136239 0.1374964 -0.9909
SmokerStatusEx-smoker 0.084901 0.1295999 0.6551
SmokerStatusNever smoked 0.552791 0.1842708 2.9999
Med.Statin.LLDyes 0.100217 0.1448559 0.6918
Med.all.antiplateletyes -0.062565 0.2065019 -0.3030
GFR_MDRD -0.002856 0.0040793 -0.7002
BMI -0.003863 0.0236201 -0.1635
MedHx_CVDyes 0.161993 0.1179045 1.3739
stenose50-70% -0.667972 0.1575696 -4.2392
stenose70-90% -0.645306 0.0951343 -6.7831
stenose90-99% -0.796388 0.0979339 -8.1319
stenose100% (Occlusion) -1.570633 0.0108586 -144.6441
stenose50-99% -1.263311 0.0065156 -193.8893
stenose70-99% -1.199791 0.0071226 -168.4488
Intercepts:
Value Std. Error t value
0|1 -427.6886 0.0602 -7103.1843
1|2 -426.1533 0.1353 -3148.7832
2|3 -424.9137 0.1520 -2795.1793
3|4 -423.3275 0.1490 -2840.3173
4|5 -421.6368 0.1983 -2126.3969
Residual Deviance: 3217.477
AIC: 3265.477
Value Std. Error t value p value
currentDF[, PROTEIN] 4.183699e-01 0.0579870631 7.2148842 5.397987e-13
Age 5.896199e-03 0.0120649241 0.4887059 6.250499e-01
Gendermale 6.627466e-01 0.1286570869 5.1512641 2.587365e-07
ORdate_year -2.117629e-01 0.0008317231 -254.6074325 0.000000e+00
Hypertension.compositeyes -1.314941e-01 0.1741966173 -0.7548602 4.503328e-01
DiabetesStatusDiabetes -1.362392e-01 0.1374964136 -0.9908561 3.217558e-01
SmokerStatusEx-smoker 8.490072e-02 0.1295998539 0.6550989 5.124041e-01
SmokerStatusNever smoked 5.527907e-01 0.1842707752 2.9998828 2.700835e-03
Med.Statin.LLDyes 1.002173e-01 0.1448559028 0.6918414 4.890369e-01
Med.all.antiplateletyes -6.256453e-02 0.2065019313 -0.3029731 7.619103e-01
GFR_MDRD -2.856430e-03 0.0040792856 -0.7002280 4.837850e-01
BMI -3.862761e-03 0.0236200979 -0.1635370 8.700956e-01
MedHx_CVDyes 1.619931e-01 0.1179045387 1.3739348 1.694619e-01
stenose50-70% -6.679723e-01 0.1575695959 -4.2392205 2.242973e-05
stenose70-90% -6.453059e-01 0.0951342876 -6.7831058 1.176194e-11
stenose90-99% -7.963882e-01 0.0979338596 -8.1318987 4.226178e-16
stenose100% (Occlusion) -1.570633e+00 0.0108586007 -144.6441313 0.000000e+00
stenose50-99% -1.263311e+00 0.0065156313 -193.8892920 0.000000e+00
stenose70-99% -1.199791e+00 0.0071225894 -168.4487660 0.000000e+00
0|1 -4.276886e+02 0.0602108239 -7103.1843328 0.000000e+00
1|2 -4.261533e+02 0.1353390471 -3148.7832305 0.000000e+00
2|3 -4.249137e+02 0.1520165995 -2795.1793305 0.000000e+00
3|4 -4.233275e+02 0.1490423213 -2840.3173304 0.000000e+00
4|5 -4.216368e+02 0.1982869504 -2126.3969333 0.000000e+00
Analysis of MCP1_rank.
- processing Plaque_Vulnerability_Index
Call:
polr(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] 0.582091 0.0870366 6.6879
Age 0.007899 0.0148627 0.5315
Gendermale 0.752179 0.1888165 3.9836
ORdate_year 0.072517 0.0009023 80.3660
Hypertension.compositeyes 0.297669 0.2500139 1.1906
DiabetesStatusDiabetes -0.225881 0.2116702 -1.0671
SmokerStatusEx-smoker -0.099253 0.1832088 -0.5418
SmokerStatusNever smoked 0.325615 0.2747025 1.1853
Med.Statin.LLDyes 0.171944 0.1984399 0.8665
Med.all.antiplateletyes -0.117527 0.2979384 -0.3945
GFR_MDRD -0.004244 0.0052909 -0.8020
BMI 0.034714 0.0266318 1.3035
MedHx_CVDyes 0.198649 0.1726682 1.1505
stenose50-70% 0.067700 0.0182550 3.7086
stenose70-90% 0.743339 0.0875836 8.4872
stenose90-99% 0.631206 0.0878806 7.1825
stenose100% (Occlusion) 0.952212 0.0303750 31.3486
Intercepts:
Value Std. Error t value
0|1 144.6067 0.0362 3990.9743
1|2 146.3445 0.2214 660.9133
2|3 147.7665 0.2540 581.8450
3|4 149.3776 0.2662 561.0606
4|5 151.0046 0.3050 495.0780
Residual Deviance: 1504.004
AIC: 1548.004
Value Std. Error t value p value
currentDF[, PROTEIN] 0.582091419 0.0870365671 6.6878950 2.264035e-11
Age 0.007898933 0.0148627060 0.5314600 5.951001e-01
Gendermale 0.752178825 0.1888165035 3.9836498 6.786486e-05
ORdate_year 0.072516969 0.0009023344 80.3659565 0.000000e+00
Hypertension.compositeyes 0.297668763 0.2500138827 1.1906089 2.338071e-01
DiabetesStatusDiabetes -0.225880837 0.2116701594 -1.0671360 2.859104e-01
SmokerStatusEx-smoker -0.099253443 0.1832088094 -0.5417504 5.879905e-01
SmokerStatusNever smoked 0.325614749 0.2747025317 1.1853358 2.358847e-01
Med.Statin.LLDyes 0.171943503 0.1984399313 0.8664763 3.862290e-01
Med.all.antiplateletyes -0.117527397 0.2979383769 -0.3944688 6.932349e-01
GFR_MDRD -0.004243556 0.0052909466 -0.8020411 4.225292e-01
BMI 0.034713834 0.0266318281 1.3034717 1.924138e-01
MedHx_CVDyes 0.198649091 0.1726681767 1.1504673 2.499515e-01
stenose50-70% 0.067699597 0.0182549767 3.7085556 2.084449e-04
stenose70-90% 0.743338980 0.0875835961 8.4871941 2.116860e-17
stenose90-99% 0.631205945 0.0878805931 7.1825408 6.842764e-13
stenose100% (Occlusion) 0.952212176 0.0303749714 31.3485786 1.017279e-215
0|1 144.606657736 0.0362334225 3990.9742895 0.000000e+00
1|2 146.344455209 0.2214276176 660.9132897 0.000000e+00
2|3 147.766496239 0.2539619705 581.8449745 0.000000e+00
3|4 149.377587034 0.2662414694 561.0605566 0.000000e+00
4|5 151.004647001 0.3050118314 495.0779984 0.000000e+00
Analysis of MCP1_plasma_olink_rank.
- processing Plaque_Vulnerability_IndexNaNs producedCall:
polr(formula = as.factor(currentDF[, TRAIT]) ~ currentDF[, PROTEIN] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = currentDF, Hess = TRUE)
Coefficients:
Value Std. Error t value
currentDF[, PROTEIN] -0.0986094 0.080436 -1.2259
Age 0.0065781 NaN NaN
Gendermale 0.6887424 0.167080 4.1222
ORdate_year -0.2620245 NaN NaN
Hypertension.compositeyes 0.0889982 0.200127 0.4447
DiabetesStatusDiabetes 0.0870284 0.182946 0.4757
SmokerStatusEx-smoker 0.1794523 0.169033 1.0616
SmokerStatusNever smoked 0.4350977 0.257198 1.6917
Med.Statin.LLDyes -0.0933360 0.181545 -0.5141
Med.all.antiplateletyes 0.1114051 0.220444 0.5054
GFR_MDRD -0.0004176 NaN NaN
BMI -0.0185648 NaN NaN
MedHx_CVDyes -0.0281831 0.158037 -0.1783
stenose50-70% -0.1894136 0.144332 -1.3123
stenose70-90% -0.2623219 0.127490 -2.0576
stenose90-99% -0.4204902 0.119098 -3.5306
stenose100% (Occlusion) -1.9254360 0.002612 -737.0113
stenose50-99% -0.6738567 0.011988 -56.2098
stenose70-99% 0.0127238 0.012516 1.0166
stenose99 2.2481837 0.007339 306.3424
Intercepts:
Value Std. Error t value
0|1 -527.4437 0.0445 -11854.0374
1|2 -526.3793 0.0904 -5825.3008
2|3 -525.4283 0.1024 -5132.7681
3|4 -524.0042 0.0956 -5479.9494
4|5 -522.7674 0.1328 -3935.8237
Residual Deviance: 1811.37
AIC: 1861.37 NaNs produced Value Std. Error t value p value
currentDF[, PROTEIN] -9.860940e-02 0.080435654 -1.225941e+00 0.2202207194
Age 6.578087e-03 NaN NaN NaN
Gendermale 6.887424e-01 0.167080141 4.122228e+00 0.0000375226
ORdate_year -2.620245e-01 NaN NaN NaN
Hypertension.compositeyes 8.899819e-02 0.200126992 4.447086e-01 0.6565303576
DiabetesStatusDiabetes 8.702838e-02 0.182945547 4.757065e-01 0.6342835136
SmokerStatusEx-smoker 1.794523e-01 0.169032877 1.061642e+00 0.2883984473
SmokerStatusNever smoked 4.350977e-01 0.257198031 1.691684e+00 0.0907063358
Med.Statin.LLDyes -9.333602e-02 0.181545389 -5.141195e-01 0.6071684755
Med.all.antiplateletyes 1.114051e-01 0.220443676 5.053678e-01 0.6133005019
GFR_MDRD -4.176067e-04 NaN NaN NaN
BMI -1.856475e-02 NaN NaN NaN
MedHx_CVDyes -2.818315e-02 0.158037297 -1.783322e-01 0.8584620605
stenose50-70% -1.894136e-01 0.144332299 -1.312344e+00 0.1894041796
stenose70-90% -2.623219e-01 0.127490111 -2.057586e+00 0.0396298498
stenose90-99% -4.204902e-01 0.119098268 -3.530616e+00 0.0004145935
stenose100% (Occlusion) -1.925436e+00 0.002612492 -7.370113e+02 0.0000000000
stenose50-99% -6.738567e-01 0.011988239 -5.620981e+01 0.0000000000
stenose70-99% 1.272378e-02 0.012515915 1.016608e+00 0.3093400750
stenose99 2.248184e+00 0.007338795 3.063424e+02 0.0000000000
0|1 -5.274437e+02 0.044494859 -1.185404e+04 0.0000000000
1|2 -5.263793e+02 0.090360876 -5.825301e+03 0.0000000000
2|3 -5.254283e+02 0.102367425 -5.132768e+03 0.0000000000
3|4 -5.240042e+02 0.095622085 -5.479949e+03 0.0000000000
4|5 -5.227674e+02 0.132822872 -3.935824e+03 0.0000000000Comparison sample types OLINK-platform
We performed a pilot experiment comparing plasma and plaque-derived protein levels as measured using the OLINK platform.
My colleague, Arjan Boltjes, analyzed this. Below some graphs and some statistics.
estimate: -0.0004093809 statistic: -0.003774306 p.value: 0.9969974 parameter: 85 conf.low: -0.2110394 conf.high: 0.210257 method: Pearson’s product-moment correlation alternative: two.sided
Figure 1: Distributions of plaque and plasma MCP1 levels. Measured using the OLINK-platform (CVD-III panel). Pilot experiment with n = 88 samples. 
Figure 2: Comparison of plaque and plasma MCP1 levels. Measured using the OLINK-platform (CVD-III panel). Pilot experiment with n = 88 samples. AU = Arbitrary unit. 
Session information
Version: v1.0.13
Last update: 2020-07-07
Written by: Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
Description: Script to analyse MCP1 from the Ather-Express Biobank Study.
Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
**MoSCoW To-Do List**
The things we Must, Should, Could, and Would have given the time we have.
_M_
* DONE - analysis on plasma based on OLINK platform
* DONE - analysis on the pilot dataset on the OLINK platform, comparing plasma vs. plaque
* DONE - linear regression models (model 1 and model 2) of `MCP1_pg_ug_2015` with cytokines
* DONE - check out the difference between the measuremens of `MCP1` and `MCP1_pg_ug_2015` > `MCP1_pg_ug_2015` and `MCP1_pg_ml_2015` give similar results, `MCP1_pg_ug_2015` is more correct as this is corrected for the total amount of protein in the protein-sample used for the measurement.
* DONE - double check the plotting of the MACE
* DONE - add the statistics for the correlation of `MCP1_pg_ug_2015` with the cytokines
* DONE - add the comparison between `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`
* DONE - analysis in the context of year of surgery given Van Lammeren _et al._
* DONE - add analysis on vulnerability index
* DONE - add analysis on binary and ordinal plaque phenotypes
* DONE - add boxplots of MCP1 levels stratified by confounders/variables
_S_
* DONE prettify forest plot
_C_
_W_
**Changes log**
* v1.0.14 Add analysis on plasma based MCP1 levels measured through OLINK, n ± 700, limited to symptomatic patients only.
* v1.0.13 Splitting RMDs into plaque-focused, and one including plasma levels of MCP1.
* v1.0.12 Add boxplots of MCP1 levels stratified by confounder/variables.
* v1.0.11 Add analysis of pilot data comparing OLINK-platform based MCP1 levels in plasma and plaque.
* v1.0.10 Add analyses for all three `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`. Add comparison between `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`. Add (and fixed) ordinal regression. Double checked which measurement to use.
* v1.0.9 Added linear regression models for MCP1 vs. cytokines plaque levels. Double checked upload of MACE-plots. Added statistics from correlation (heatmap) to txt-file.
* v1.0.8 Fixed error in MCP1 plasma analysis. It turns out the `MCP1` and `MCP1_pg_ug_2015` variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively.
* v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
* v1.0.6 Only analyses and figures that end up in the final manuscript.
* v1.0.5 Update with 30- and 90-days survival.
* v1.0.4 Updated with Cox-regressions.
* v1.0.3 Included more models.
* v1.0.2 Bugs fixed.
* v1.0.1 Extended with linear and logistic regressions.
* v1.0.0 Inital version.sessionInfo()R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin19.4.0 (64-bit)
Running under: macOS Catalina 10.15.5
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /usr/local/Cellar/openblas/0.3.10/lib/libopenblasp-r0.3.10.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] tools stats graphics grDevices utils datasets methods base
other attached packages:
[1] GGally_1.5.0 PerformanceAnalytics_2.0.4 xts_0.12-0 zoo_1.8-8 ggcorrplot_0.1.3.999
[6] Hmisc_4.4-0 Formula_1.2-3 lattice_0.20-41 survminer_0.4.6 survival_3.1-12
[11] scales_1.1.1 ggsci_2.9 patchwork_1.0.1.9000 LDlinkR_1.0.2.9000 RACER_1.0.0
[16] openxlsx_4.1.5 ggpubr_0.3.0 tableone_0.11.1 labelled_2.4.0 sjPlot_2.8.4
[21] sjlabelled_1.1.5 haven_2.3.0 devtools_2.3.0 usethis_1.6.1 MASS_7.3-51.6
[26] DT_0.13 knitr_1.28 forcats_0.5.0 stringr_1.4.0 purrr_0.3.4
[31] tibble_3.0.1 ggplot2_3.3.0 tidyverse_1.3.0 data.table_1.12.8 naniar_0.5.1
[36] tidyr_1.1.0 dplyr_0.8.5 optparse_1.6.6 readr_1.3.1
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.7 plyr_1.8.6 splines_3.6.3 crosstalk_1.1.0.1 TH.data_1.0-10 inline_0.3.15
[8] digest_0.6.25 htmltools_0.4.0 fansi_0.4.1 checkmate_2.0.0 magrittr_1.5 memoise_1.1.0 cluster_2.1.0
[15] remotes_2.1.1 modelr_0.1.8 matrixStats_0.56.0 sandwich_2.5-1 prettyunits_1.1.1 jpeg_0.1-8.1 colorspace_1.4-1
[22] rvest_0.3.5 mitools_2.4 xfun_0.14 callr_3.4.3 crayon_1.3.4 jsonlite_1.6.1 lme4_1.1-23
[29] glue_1.4.1 gtable_0.3.0 emmeans_1.4.7 sjstats_0.18.0 sjmisc_2.8.4 car_3.0-8 pkgbuild_1.0.8
[36] rstan_2.19.3 abind_1.4-5 mvtnorm_1.1-0 DBI_1.1.0 rstatix_0.5.0.999 ggeffects_0.14.3 Rcpp_1.0.4.6
[43] htmlTable_1.13.3 xtable_1.8-4 performance_0.4.6 foreign_0.8-75 km.ci_0.5-2 stats4_3.6.3 StanHeaders_2.19.2
[50] survey_4.0 htmlwidgets_1.5.1 httr_1.4.1 getopt_1.20.3 RColorBrewer_1.1-2 acepack_1.4.1 ellipsis_0.3.1
[57] reshape_0.8.8 pkgconfig_2.0.3 loo_2.2.0 farver_2.0.3 nnet_7.3-14 dbplyr_1.4.3 tidyselect_1.1.0
[64] labeling_0.3 rlang_0.4.6 reshape2_1.4.4 effectsize_0.3.1 munsell_0.5.0 cellranger_1.1.0 cli_2.0.2
[71] generics_0.0.2 broom_0.5.6 evaluate_0.14 yaml_2.2.1 processx_3.4.2 fs_1.4.1 zip_2.0.4
[78] survMisc_0.5.5 packrat_0.5.0 visdat_0.5.3 nlme_3.1-148 xml2_1.3.2 compiler_3.6.3 rstudioapi_0.11
[85] png_0.1-7 curl_4.3 e1071_1.7-3 testthat_2.3.2 ggsignif_0.6.0 reprex_0.3.0 statmod_1.4.34
[92] stringi_1.4.6 highr_0.8 ps_1.3.3 parameters_0.7.0 desc_1.2.0 Matrix_1.2-18 nloptr_1.2.2.1
[99] KMsurv_0.1-5 vctrs_0.3.0 pillar_1.4.4 lifecycle_0.2.0 estimability_1.3 cowplot_1.0.0 insight_0.8.4
[106] latticeExtra_0.6-29 R6_2.4.1 gridExtra_2.3 rio_0.5.16 sessioninfo_1.1.1 codetools_0.2-16 boot_1.3-25
[113] assertthat_0.2.1 pkgload_1.0.2 rprojroot_1.3-2 withr_2.2.0 multcomp_1.4-13 mgcv_1.8-31 bayestestR_0.6.0
[120] parallel_3.6.3 hms_0.5.3 quadprog_1.5-8 rpart_4.1-15 grid_3.6.3 class_7.3-17 coda_0.19-3
[127] minqa_1.2.4 rmarkdown_2.1 carData_3.0-4 lubridate_1.7.8 base64enc_0.1-3 Saving environment
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))| © 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | swvanderlaan.github.io. |
---
title: "Athero-Express Biobank Study -- IL6 and MCP1 plaque levels."
author: '[Sander W. van der Laan, PhD](https://swvanderlaan.github.io) | @swvanderlaan; Marios Georgakis; Rainer Malik; Martin Dichgans'
date: '`r Sys.Date()`'
output:
  html_notebook:
    cache: yes
    code_folding: hide
    collapse: yes
    df_print: paged
    fig.align: center
    fig_caption: yes
    fig_height: 10
    fig_retina: 2
    fig_width: 12
    theme: paper
    toc: yes
    toc_float:
      collapsed: no
      smooth_scroll: yes
mainfont: Helvetica
subtitle: An 'Athero-Express Biobank Study' project
editor_options:
  chunk_output_type: inline
---
```{r global_options, include = FALSE}
# further define some knitr-options.
knitr::opts_chunk$set(fig.width = 12, fig.height = 8, fig.path = 'Figures/',
                      eval = TRUE, warning = FALSE, message = FALSE)
```

# Preparation

Clean the environment.
```{r ClearEnvironment, include = FALSE}
rm(list = ls())
```

Set locations, and the working directory.
```{r LocalSystem, include = FALSE}
### Operating System Version
### Mac Pro
# ROOT_loc = "/Volumes/EliteProQx2Media"
# GENOMIC_loc = "/Users/svanderlaan/iCloud/Genomics"

### MacBook
ROOT_loc = "/Users/swvanderlaan"
GENOMIC_loc = paste0(ROOT_loc, "/iCloud/Genomics")

### Generic Locations
AEDB_loc = paste0(GENOMIC_loc, "/AE-AAA_GS_DBs")
LAB_loc = paste0(GENOMIC_loc, "/LabBusiness")
RESULTS = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")
RAWDATA = paste0(ROOT_loc, "/PLINK/_AE_ORIGINALS/AESCRNA/prepped_data")
PROJECT_loc = paste0(ROOT_loc, "/PLINK/analyses/lookups/AE_20190912_010_MDICHGANS_SWVDLAAN_IL6_MCP1")

### SOME VARIABLES WE NEED DOWN THE LINE
cat("\nDefining phenotypes and datasets.\n")
PROJECTNAME="IL6MCP1"
# SUBPROJECTNAME=""

cat("\nCreate a new analysis directory, including subdirectories.\n")
# Analysis
ifelse(!dir.exists(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       dir.create(file.path(PROJECT_loc, "/",PROJECTNAME)), 
       FALSE)
ANALYSIS_loc = paste0(PROJECT_loc,"/",PROJECTNAME)

# Plots
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")), 
       dir.create(file.path(ANALYSIS_loc, "/PLOTS")), 
       FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")

# QC plots
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")), 
       dir.create(file.path(PLOT_loc, "/QC")), 
       FALSE)
QC_loc = paste0(PLOT_loc,"/QC")

# Output files
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")), 
       dir.create(file.path(ANALYSIS_loc, "/OUTPUT")), 
       FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")

# COX analysis
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/COX")), 
       dir.create(file.path(ANALYSIS_loc, "/COX")), 
       FALSE)
COX_loc = paste0(ANALYSIS_loc, "/COX")

# Baseline characteristics
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/BASELINE")), 
       dir.create(file.path(ANALYSIS_loc, "/BASELINE")), 
       FALSE)
BASELINE_loc = paste0(ANALYSIS_loc, "/BASELINE")

# Sample selection
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       dir.create(file.path(ANALYSIS_loc, "/SELECTIONS")), 
       FALSE)
SELECTIONS_loc = paste0(ANALYSIS_loc, "/SELECTIONS")

cat("\nSetting working directory and listing its contents.\n")
setwd(paste0(PROJECT_loc))
getwd()
list.files()
```

A package-installation function.
```{r Function: installations, include = FALSE}
install.packages.auto <- function(x) { 
  x <- as.character(substitute(x)) 
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else { 
    # Update installed packages - this may mean a full upgrade of R, which in turn
    # may not be warrented. 
    # update.install.packages.auto(ask = FALSE) 
    eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"https://cloud.r-project.org/\")", x)))
  }
  if(isTRUE(x %in% .packages(all.available = TRUE))) { 
    eval(parse(text = sprintf("require(\"%s\")", x)))
  } else {
    if (!requireNamespace("BiocManager"))
      install.packages("BiocManager")
    # BiocManager::install() # this would entail updating installed packages, which in turned may not be warrented
    eval(parse(text = sprintf("BiocManager::install(\"%s\")", x)))
    eval(parse(text = sprintf("require(\"%s\")", x)))
  }
}
```

Load those packages.
```{r Setting: loading_packages, include = FALSE}
install.packages.auto("readr")
install.packages.auto("optparse")
install.packages.auto("tools")
install.packages.auto("dplyr")
install.packages.auto("tidyr")
install.packages.auto("naniar")

# To get 'data.table' with 'fwrite' to be able to directly write gzipped-files
# Ref: https://stackoverflow.com/questions/42788401/is-possible-to-use-fwrite-from-data-table-with-gzfile
# install.packages("data.table", repos = "https://Rdatatable.gitlab.io/data.table")
library(data.table)

install.packages.auto("tidyverse")
install.packages.auto("knitr")
install.packages.auto("DT")
install.packages.auto("MASS")
# install.packages.auto("Seurat") # latest version

# Install the devtools package from Hadley Wickham
install.packages.auto('devtools')

install.packages.auto("haven")
install.packages.auto("sjlabelled")
install.packages.auto("sjPlot")
install.packages.auto("labelled")
install.packages.auto("tableone")

install.packages.auto("ggpubr")

```

We will create a datestamp and define the Utrecht Science Park Colour Scheme.
```{r Setting: Colors, include = FALSE}

Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")

### UtrechtScienceParkColoursScheme
###
### WebsitetoconvertHEXtoRGB:http://hex.colorrrs.com.
### Forsomefunctionsyoushoulddividethesenumbersby255.
### 
###	No.	Color			      HEX	(RGB)						              CHR		  MAF/INFO
###---------------------------------------------------------------------------------------
###	1	  yellow			    #FBB820 (251,184,32)				      =>	1		or 1.0>INFO
###	2	  gold			      #F59D10 (245,157,16)				      =>	2		
###	3	  salmon			    #E55738 (229,87,56)				      =>	3		or 0.05<MAF<0.2 or 0.4<INFO<0.6
###	4	  darkpink		    #DB003F ((219,0,63)				      =>	4		
###	5	  lightpink		    #E35493 (227,84,147)				      =>	5		or 0.8<INFO<1.0
###	6	  pink			      #D5267B (213,38,123)				      =>	6		
###	7	  hardpink		    #CC0071 (204,0,113)				      =>	7		
###	8	  lightpurple	    #A8448A (168,68,138)				      =>	8		
###	9	  purple			    #9A3480 (154,52,128)				      =>	9		
###	10	lavendel		    #8D5B9A (141,91,154)				      =>	10		
###	11	bluepurple		  #705296 (112,82,150)				      =>	11		
###	12	purpleblue		  #686AA9 (104,106,169)			      =>	12		
###	13	lightpurpleblue	#6173AD (97,115,173/101,120,180)	=>	13		
###	14	seablue			    #4C81BF (76,129,191)				      =>	14		
###	15	skyblue			    #2F8BC9 (47,139,201)				      =>	15		
###	16	azurblue		    #1290D9 (18,144,217)				      =>	16		or 0.01<MAF<0.05 or 0.2<INFO<0.4
###	17	lightazurblue	  #1396D8 (19,150,216)				      =>	17		
###	18	greenblue		    #15A6C1 (21,166,193)				      =>	18		
###	19	seaweedgreen	  #5EB17F (94,177,127)				      =>	19		
###	20	yellowgreen		  #86B833 (134,184,51)				      =>	20		
###	21	lightmossgreen	#C5D220 (197,210,32)				      =>	21		
###	22	mossgreen		    #9FC228 (159,194,40)				      =>	22		or MAF>0.20 or 0.6<INFO<0.8
###	23	lightgreen	  	#78B113 (120,177,19)				      =>	23/X
###	24	green			      #49A01D (73,160,29)				      =>	24/Y
###	25	grey			      #595A5C (89,90,92)				        =>	25/XY	or MAF<0.01 or 0.0<INFO<0.2
###	26	lightgrey		    #A2A3A4	(162,163,164)			      =>	26/MT
###
###	ADDITIONAL COLORS
###	27	midgrey			#D7D8D7
###	28	verylightgrey	#ECECEC"
###	29	white			#FFFFFF
###	30	black			#000000
###----------------------------------------------------------------------------------------------

uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
                 "#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
                 "#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
                 "#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
                 "#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
                        "#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
                        "#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
                        "#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
                        "#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
                        "#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")

#ggplot2 default color palette
gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

### ----------------------------------------------------------------------------
```


```{r Analysis Functions}
# Function to grep data from glm()/lm()
GLM.CON <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' .\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    tvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    R = summary(fit)$r.squared
    R.adj = summary(fit)$adj.r.squared
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, tvalue, pvalue, R, R.adj, AE_N, sample_size, Perc_Miss)
    
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("T-value...................:", round(tvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("R^2.......................:", round(R, 6), "\n")
    cat("Adjusted r^2..............:", round(R.adj, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}

GLM.BIN <- function(fit, DATASET, x_name, y, verbose=c(TRUE,FALSE)){
  cat("Analyzing in dataset '", DATASET ,"' the association of '", x_name ,"' with '", y ,"' ...\n")
  if (nrow(summary(fit)$coefficients) == 1) {
    output = c(DATASET, x_name, y, rep(NA,9))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data...\n")
    effectsize = summary(fit)$coefficients[2,1]
    SE = summary(fit)$coefficients[2,2]
    OReffect = exp(summary(fit)$coefficients[2,1])
    CI_low = exp(effectsize - 1.96 * SE)
    CI_up = exp(effectsize + 1.96 * SE)
    zvalue = summary(fit)$coefficients[2,3]
    pvalue = summary(fit)$coefficients[2,4]
    dev <- fit$deviance
    nullDev <- fit$null.deviance
    modelN <- length(fit$fitted.values)
    R.l <- 1 - dev / nullDev
    R.cs <- 1 - exp(-(nullDev - dev) / modelN)
    R.n <- R.cs / (1 - (exp(-nullDev/modelN)))
    sample_size = nrow(model.frame(fit))
    AE_N = AEDB.CEA.samplesize
    Perc_Miss = 100 - ((sample_size * 100)/AE_N)
    
    output = c(DATASET, x_name, y, effectsize, SE, OReffect, CI_low, CI_up, zvalue, pvalue, R.l, R.cs, R.n, AE_N, sample_size, Perc_Miss)
    if (verbose == TRUE) {
    cat("We have collected the following and summarize it in an object:\n")
    cat("Dataset...................:", DATASET, "\n")
    cat("Score/Exposure/biomarker..:", x_name, "\n")
    cat("Trait/outcome.............:", y, "\n")
    cat("Effect size...............:", round(effectsize, 6), "\n")
    cat("Standard error............:", round(SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(OReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(CI_up, 3), "\n")
    cat("Z-value...................:", round(zvalue, 6), "\n")
    cat("P-value...................:", signif(pvalue, 8), "\n")
    cat("Hosmer and Lemeshow r^2...:", round(R.l, 6), "\n")
    cat("Cox and Snell r^2.........:", round(R.cs, 6), "\n")
    cat("Nagelkerke's pseudo r^2...:", round(R.n, 6), "\n")
    cat("Sample size of AE DB......:", AE_N, "\n")
    cat("Sample size of model......:", sample_size, "\n")
    cat("Missing data %............:", round(Perc_Miss, 6), "\n")
    } else {
      cat("Collecting data in summary object.\n")
    }
  }
  return(output)
  print(output)
}
```


# Background

Using a Mendelian Randomization approach, we recently examined associations between the circulating levels of 41 cytokines and growth factors and the risk of stroke in the MEGASTROKE GWAS dataset (67,000 stroke cases and 450,000 controls) and found Monocyte chemoattractant protein-1 (MCP-1) as the cytokine showing the strongest association with stroke, particularly large artery and cardioembolic stroke (Georgakis et al., 2019a). Genetically elevated MCP-1 levels were also associated with a higher risk of coronary artery disease and myocardial infarction (Georgakis et al., 2019a). Further, in a meta-analysis of 6 observational population-based of longitudinal cohort studies we recently showed that baseline levels of MCP-1 were associated with a higher risk of ischemic stroke over follow-up (Georgakis et al., 2019b).
While these data suggest a central role of MCP-1 in the pathogenesis of atherosclerosis, it remains unknown if MCP-1 levels in the blood really reflect MCP-1 activity. MCP-1 is expressed in the atherosclerotic plaque and attracts monocytes in the subendothelial space (Nelken et al., 1991; Papadopoulou et al., 2008; Takeya et al., 1993; Wilcox et al., 1994). Thus, MCP-1 levels in the plaque might more strongly reflect MCP-1 signaling. However, it remains unknown if MCP-1 plaque levels associate with plaque vulnerability or risk of cardiovascular events.


## Objectives

Against this background we now aim to make use of the data from Athero-Express Biobank Study to explore the associations of MCP-1 protein levels in the atherosclerotic plaques from patients undergoing carotid endarterectomy with phenotypes of plaque vulnerability and secondary vascular events over a follow-up of three years.


## Methods

*Blood*

OLINK-platform 

- IL6: Interleukin 6. Entrez Gene: 3569. OLINK, plasma <!-- Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL] -->
- MCP1: Monocyte chemotactic protein 1, MCP-1 (Chemokine (C-C motif) ligand 2, CCL2). Entrez Gene: 6347. OLINK, plasma <!-- Measured at the WKZ. Recalculated Luminex. [pg/mL] -->

> THESE DATA ARE NOT AVAILABLE YET

*Plaque*

Luminex-platform, measured by Luminex

- MCP1: Monocyte chemotactic protein 1 (a.k.a. CCL2; Entrez Gene: 6347) concentration in plaque [pg/ug]. Measured in two experiments, variables `MCP1` and `MCP1_pg_ug_2015`. The latter was corrected for plaque total protein concentration.
- IL6: Interleuking 6 (IL6; Entrez Gene: 3569) concentration in plaque [pg/ug].
- IL6R: Interleuking 6 receptor (IL6R; Entrez Gene: 3570) concentration in plaque [pg/ug].

FACS platform

- IL6: Interleukin 6. Entrez Gene: 3569. Bender MedSystems; cat.nr.: BMS810FF. Recalculated FACS. [pg/mL]


# Loading data

## Clinical data

Loading Athero-Express clinical data.
```{r LoadAEDB}
require(haven)

# AEDB <- haven::read_sav(paste0(AEDB_loc, "/2019-3NEW_AtheroExpressDatabase_ScientificAE_02072019_IC_added.sav"))
AEDBraw <- haven::read_sav(paste0(AEDB_loc, "/2020_1_NEW_AtheroExpressDatabase_ScientificAE_16-03-2020.sav"))

head(AEDBraw)
```

## Plaque protein data
Loading Athero-Express plaque protein measurements from 2015.
```{r LoadAE PlaqueProteins}
library(openxlsx)
AEDB_Protein_2015 <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_Proteins/Cytokines_and_chemokines_2015/20200629_MPCF015-0024.xlsx"), sheet = "for_SPSS_R")

names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "SampleID"] <- "STUDY_NUMBER"

head(AEDB_Protein_2015)

```

## Plasma protein data
Loading Athero-Express plasma protein measurements from 2019/2020 as measured using OLINK.
```{r LoadAE PlasmaProteins OLINK}
library(openxlsx)
AEDB_PlasmaProtein_OLINK_CVD2raw <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "CVD2_forR")
AEDB_PlasmaProtein_OLINK_CVD3raw <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "CVD3_forR")
AEDB_PlasmaProtein_OLINK_CMraw <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "CM_forR")

AEDB_PlasmaProtein_OLINK_ProteinInfo <- openxlsx::read.xlsx(paste0(AEDB_loc, "/_AE_OLINK/20200706_AtheroExpress_OlinkData_forR.xlsx"), sheet = "ProteinInfo")

AEDB_PlasmaProtein_OLINK_CVD2 <- AEDB_PlasmaProtein_OLINK_CVD2raw %>% filter(QC_Warning_CVD2 == "Pass")
AEDB_PlasmaProtein_OLINK_CVD3 <- AEDB_PlasmaProtein_OLINK_CVD3raw %>% filter(QC_Warning_CVD3 == "Pass")
AEDB_PlasmaProtein_OLINK_CM <- AEDB_PlasmaProtein_OLINK_CMraw %>% filter(QC_Warning_CM == "Pass")

table(AEDB_PlasmaProtein_OLINK_CVD2raw$QC_Warning_CVD2)
table(AEDB_PlasmaProtein_OLINK_CVD2$QC_Warning_CVD2)

table(AEDB_PlasmaProtein_OLINK_CVD3raw$QC_Warning_CVD3)
table(AEDB_PlasmaProtein_OLINK_CVD3$QC_Warning_CVD3)

table(AEDB_PlasmaProtein_OLINK_CMraw$QC_Warning_CM)
table(AEDB_PlasmaProtein_OLINK_CM$QC_Warning_CM)

AEDB_PlasmaProtein_OLINK_CVD2$Plate_ID <- NULL
AEDB_PlasmaProtein_OLINK_CVD3$Plate_ID <- NULL
AEDB_PlasmaProtein_OLINK_CVD2$Order <- NULL
AEDB_PlasmaProtein_OLINK_CVD3$Order <- NULL
AEDB_PlasmaProtein_OLINK_CM$Order <- NULL

temp <- merge(AEDB_PlasmaProtein_OLINK_CVD2, AEDB_PlasmaProtein_OLINK_CVD3, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER",
              sort = FALSE, all.x = TRUE)

AEDB_PlasmaProtein_OLINK <- merge(temp, AEDB_PlasmaProtein_OLINK_CM, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER",
              sort = FALSE, all.x = TRUE)

AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_cardiometabolic_plt1_29-10-19"] <- "plate 1"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt2"] <- "plate 2"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt3"] <- "plate 3"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt4"] <- "plate 4"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_plt5"] <- "plate 5"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "ERA_UMC_AE_Cardiometabolic_pl6"] <- "plate 6"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "SMART_CM_plt10"] <- "plate 10"
AEDB_PlasmaProtein_OLINK$Plate_ID[AEDB_PlasmaProtein_OLINK$Plate_ID == "SMART_plt11_CM"] <- "plate 11"

olink_proteins <- c("BMP6", "ANGPT1", "ADM", "CD40L", "SLAMF7", "PGF", "ADAMTS13", "BOC", "IL4RA", "SRC", "IL1ra", "IL6", "TNFRSF10A", "STK4", "IDUA", 
                    "TNFRSF11A", "PAR1", "TRAILR2", "PRSS27", "TIE2", "TF", "IL1RL2", "PDGF_subunit_B", "IL27", "IL17D", "CXCL1", "LOX1", "Gal9", "GIF", "SCF", 
                    "IL18", "FGF21", "PIgR", "RAGE", "SOD2", "CTRC", "FGF23", "SPON2", "GH", "FS", "GLO1", "CD84", "PAPPA", "SERPINA12", "REN", "DECR1", 
                    "MERTK", "KIM1", "THBS2", "TM", "VSIG2", "AMBP", "PRELP", "HO1", "XCL1", "IL16", "SORT1", "CEACAM8", "PTX3", "PSGL1", "CCL17", "CCL3", 
                    "MMP7", "IgG_Fc_receptor_IIb", "ITGB1BP2", "DCN", "Dkk1", "LPL", "PRSS8", "AGRP", "HBEGF", "GDF2", "FABP2", "THPO", "MARCO", "GT", "BNP", 
                    "MMP12", "ACE2", "PDL2", "CTSL1", "hOSCAR", "TNFRSF13B", "TGM2", "LEP", "CA5A", "HSP_27", "CD4", "NEMO", "VEGFD", "PARP1", "HAOX1", 
                    "TNFRSF14", "LDL_receptor", "ITGB2", "IL17RA", "TNFR2", "MMP9", "EPHB4", "IL2RA", "OPG", "ALCAM", "TFF3", "SELP", "CSTB", "MCP1", "CD163", 
                    "Gal3", "GRN", "NTproBNP", "BLM_hydrolase", "PLC", "LTBR", "Notch_3", "TIMP4", "CNTN1", "CDH5", "TLT2", "FABP4", "TFPI", "PAI", "CCL24", 
                    "TR", "TNFRSF10C", "GDF15", "SELE", "AZU1", "DLK1", "SPON1", "MPO", "CXCL16", "IL6RA", "RETN", "IGFBP1", "CHIT1", "TRAP", "GP6", "PSPD", 
                    "PI3", "EpCAM", "APN", "AXL", "IL1RT1", "MMP2", "FAS", "MB", "TNFSF13B", "PRTN3", "PCSK9", "UPAR", "OPN", "CTSD", "PGLYRP1", "CPA1", "JAMA", 
                    "Gal4", "IL1RT2", "SHPS1", "CCL15", "CASP3", "uPA", "CPB1", "CHI3L1", "ST2", "tPA", "SCGB3A2", "EGFR", "IGFBP7", "CD93", "IL18BP", "COL1A1", 
                    "PON3", "CTSZ", "MMP3", "RARRES2", "ICAM2", "KLK6", "PDGF_subunit_A", "TNFR1", "IGFBP2", "vWF", "PECAM1", "MEPE", "CCL16", "PRCP", "CA1", 
                    "ICAM1", "CHL1", "TGFBI", "ENG", "PLTP", "SERPINA7", "IGFBP3", "CR2", "SERPINA5", "FCGR3B", "IGFBP6", "CDH1", "CCL5", "CCL14", "GNLY", 
                    "NOTCH1", "PAM", "PROC", "CST3", "NCAM1", "PCOLCE", "LILRB1", "MET", "LTBP2", "IL7R", "VCAM1", "SELL", "F11", "COMP", "CA4", "PTPRS", 
                    "MBL2", "TIMP1", "ANGPTL3", "REG3A", "SOD1", "CD46", "ITGAM", "TNC", "NID1", "CFHR5", "SPARCL1", "PLXNB2", "MEGF9", "ANG", "ST6GAL1", 
                    "DPP4", "REG1A", "QPCT", "FCN2", "FETUB", "CES1", "CRTAC1", "TCN2", "PRSS2", "ICAM3", "SAA4", "CNDP1", "FCGR2A", "NRP1", "EFEMP1", "TIMD4", 
                    "FAP", "TIE1", "THBS4", "F7", "GP1BA", "LYVE1", "CA3", "TGFBR3", "DEFA1", "CD59", "APOM", "OSMR", "LILRB2", "UMOD", "CCL18", "COL18A1", 
                    "LCN2", "KIT", "C1QTNF1", "AOC3", "GAS6", "IGLC2", "PLA2G7", "TNXB", "MFAP5", "VASN", "LILRB5", "C2")

length(olink_proteins)

olink_proteins_rank = unlist(lapply(olink_proteins, paste0, "_rankNorm"))

olink_proteins_short <- c("MCP1")
olink_proteins_short_rank <- unlist(lapply(olink_proteins_short, paste0, "_rankNorm"))

rm(temp)

```

### Inspect OLINK data

We know that the proteins are not normally distributed and therefore we will standardise them as follows: 

`z = ( x - μ ) / σ`

Where for each sample, `x` equals the value of the variable, `μ` (_mu_) equals the mean of `x`, and `σ` (_sigma_) equals the standard deviation of `x`.

```{r OLINKStandardize}
for(PROTEIN in 1:length(olink_proteins_short)){
  # AEDB_PlasmaProtein_OLINK$Z <- NULL
  var.temp.z = olink_proteins_short_rank[PROTEIN]
  var.temp = olink_proteins_short[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.z,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  AEDB_PlasmaProtein_OLINK <- AEDB_PlasmaProtein_OLINK %>%
    mutate_each_(funs(as.numeric), olink_proteins_short) 
  
  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AEDB_PlasmaProtein_OLINK[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AEDB_PlasmaProtein_OLINK[,var.temp])),").\n"))
  
  AEDB_PlasmaProtein_OLINK <- AEDB_PlasmaProtein_OLINK %>%
      mutate_at(vars(var.temp), 
        list(Z = ~ (AEDB_PlasmaProtein_OLINK[,var.temp] - mean(AEDB_PlasmaProtein_OLINK[,var.temp], na.rm = TRUE))/sd(AEDB_PlasmaProtein_OLINK[,var.temp], na.rm = TRUE))
      )
  # str(AEDB_PlasmaProtein_OLINK$Z)
  cat(paste0("* renaming Z to ", var.temp.z,".\n"))
  AEDB_PlasmaProtein_OLINK[,var.temp.z] <- NULL
  names(AEDB_PlasmaProtein_OLINK)[names(AEDB_PlasmaProtein_OLINK) == "Z"] <- var.temp.z
}

rm(var.temp, var.temp.z)
```


Here we summarize some of these data in the subset of genetic data that passed QC.
```{r OLINKSummary}

for(PROTEIN in 1:length(olink_proteins_short)){
  var.temp.z = olink_proteins_short_rank[PROTEIN]
  var.temp = olink_proteins_short[PROTEIN]
  
  cat(paste0("\nSummarising data for ",var.temp," [AU]; n = ",sum(!is.na(AEDB_PlasmaProtein_OLINK[,var.temp])),".\n"))
  print(summary(AEDB_PlasmaProtein_OLINK[,var.temp]))
  print(summary(AEDB_PlasmaProtein_OLINK[,var.temp.z]))

}
rm(var.temp, var.temp.z, PROTEIN)

```

```{r OLINKVisualize }
require("ggpubr")
require("ggsci")

# mypal = pal_npg("nrc", alpha = 0.7)(9)
# mypal
# ## [1] "#E64B35B2" "#4DBBD5B2" "#00A087B2" "#3C5488B2" "#F39B7FB2" "#8491B4B2"
# ## [7] "#91D1C2B2" "#DC0000B2" "#7E6148B2"
# library("scales")
# show_col(mypal)

for(PROTEIN in 1:length(olink_proteins_short)){
  # metabolite_unit = ucorbioNMRDataDictionary[ucorbioNMRDataDictionary$Metabolite_NMR == NMRtargets[METABOLITE], "Concentration_NMR"]
  cat(paste0("\nProcessing metabolite [ ",olink_proteins_short[PROTEIN]," (AU)].\n"))

  var.temp = olink_proteins_short[PROTEIN]
  var.temp.z = paste0(olink_proteins_short[PROTEIN],"_rankNorm")
   
  dt.temp <- subset(AEDB_PlasmaProtein_OLINK, select = c("STUDY_NUMBER", var.temp, var.temp.z, "Plate_ID"))
  dt.temp[,2] <- as.numeric(dt.temp[,2])
  
  p1 <- ggpubr::gghistogram(dt.temp %>% filter(!is.na(Plate_ID)),
                    x = var.temp,
                    y = "..count..",
                    color = "#4DBBD5B2", fill = "#4DBBD5B2",
                    # palette = "npg",
                    rug = TRUE,
                    add = "mean",
                    xlab = paste0(var.temp," [AU]."),
                    ggtheme = theme_minimal())
  
  my_comparisons <- list( c("plate 1", "plate 2"), 
                          c("plate 1", "plate 3"), 
                          c("plate 1", "plate 4"), 
                          c("plate 1", "plate 5"), 
                          c("plate 1", "plate 6"), 
                          c("plate 1", "plate 10"), 
                          c("plate 1", "plate 11") )
  p2 <- ggpubr::ggboxplot(data = dt.temp %>% filter(!is.na(Plate_ID)),
                          x = "Plate_ID",
                          y = var.temp.z,
                          color = "Plate_ID",
                          palette = "npg",
                          add = c("mean", "jitter"),
                          # error.plot = "errorbar",
                          xlab = "plates used",
                          ylab = paste0(var.temp.z," [AU]."),
                          # ylim = c(0,4),
                          ggtheme = theme_minimal()) #+
    # stat_compare_means(method = "anova") #+      # Add global p-value
    # stat_compare_means(comparisons = my_comparisons) + # Add pairwise comparisons p-value
    # stat_compare_means(label = "p.signif", method = "t.test", ref.group = "plate 1")
  
  
  p3 <- ggpubr::gghistogram(dt.temp %>% filter(!is.na(Plate_ID)),
                    x = var.temp.z,
                    y = "..count..",
                    color = "#91D1C2B2", fill = "#91D1C2B2",
                    # palette = "npg",
                    rug = TRUE,
                    add = "mean",
                    xlab = paste0(var.temp.z," [AU]."),
                    ggtheme = theme_minimal())
  
  require(patchwork)
  # p4 <- ((p1 / p3 ) | (p2))
  
  p4 <- ggpar(p1, legend = "" ) / ggpar(p2 + rotate_x_text(45), legend = "")  | ggpar(p3, legend = "right")
  
  print(p4)
  ggsave(filename = paste0(QC_loc, "/",Today,".",PROJECTNAME,".OLINK.",var.temp,".png"),
         plot = p4, device = "png", width = 20, height = 20)
  
}
  # rm(my_comparisons,
  #    p1, p2, p3, p4,
  #    var.temp, var.temp.z, dt.temp, PROTEIN)

```




## Merging protein data
We will merge these measurements to the AEDB for comparing pg/ug vs. pg/mL measurements of MCP1 - also in relation to plaque phenotypes. In addition we have more information the experiment and can correct for this.

```{r merge AEDB and Proteins}
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6_pg_ml"] <- "IL6_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL6R_pg_ml"] <- "IL6R_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "IL8_pg_ml"] <- "IL8_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCP1_pg_ml"] <- "MCP1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "RANTES_pg_ml"] <- "RANTES_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "PAI1_pg_ml"] <- "PAI1_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "MCSF_pg_ml"] <- "MCSF_pg_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Adiponectin_ng_ml"] <- "Adiponectin_ng_ml_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Segment_isolated_Tris"] <- "Segment_isolated_Tris_2015"
names(AEDB_Protein_2015)[names(AEDB_Protein_2015) == "Tris_protein_conc_ug_ml"] <- "Tris_protein_conc_ug_ml_2015"

temp <- subset(AEDB_Protein_2015, select = c("STUDY_NUMBER", "IL6_pg_ml_2015", "IL6R_pg_ml_2015", "IL8_pg_ml_2015", "MCP1_pg_ml_2015", "RANTES_pg_ml_2015", "PAI1_pg_ml_2015", "MCSF_pg_ml_2015", "Adiponectin_ng_ml_2015", "Segment_isolated_Tris_2015", "Tris_protein_conc_ug_ml_2015"))

temp2 <- subset(AEDB_PlasmaProtein_OLINK, select = c("STUDY_NUMBER", "MCP1", "MCP1_rankNorm", "Plate_ID"))
names(temp2)[names(temp2) == "MCP1"] <- "MCP1_plasma_olink"
names(temp2)[names(temp2) == "MCP1_rankNorm"] <- "MCP1_plasma_olink_rankNorm"
names(temp2)[names(temp2) == "Plate_ID"] <- "PlateID_plasma_olink"


AEDBraw2 <- merge(AEDBraw, temp, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
              all.x = TRUE)

AEDB <- merge(AEDBraw2, temp2, by.x = "STUDY_NUMBER", by.y = "STUDY_NUMBER", sort = FALSE,
              all.x = TRUE)
rm(temp, temp2, AEDBraw2)

temp <- subset(AEDB, select = c("STUDY_NUMBER", "MCP1", "MCP1_pg_ug_2015", "MCP1_pg_ml_2015", "Segment_isolated_Tris_2015",
                                "MCP1_plasma_olink", "MCP1_plasma_olink_rankNorm", "PlateID_plasma_olink"))
dim(temp)
head(temp)
rm(temp)   
```

#### Examine AEDB

We can examine the contents of the Athero-Express Biobank dataset to know what each variable is called, what class (type) it has, and what the variable description is. 

There is an excellent post on this: https://www.r-bloggers.com/working-with-spss-labels-in-r/. 
```{r AEDB: describe}
AEDB %>% sjPlot::view_df(show.type = TRUE,
                         show.frq = TRUE,
                         show.prc = TRUE,
                         show.na = TRUE, 
                         max.len = TRUE, 
                         wrap.labels = 20,
                         verbose = FALSE, 
                         use.viewer = FALSE,
                         file = paste0(OUT_loc, "/", Today, ".AEDB.dictionary.html")) 
```


## Fixing and creating variables

We need to be very strict in defining _symptoms._ Therefore we will fix a new variable that groups _symptoms_ at inclusion.

Coding of _symptoms_ is as follows:

- missing	-999	
- Asymptomatic	0	
- TIA	1	
- minor stroke	2	
- Major stroke	3	
- Amaurosis fugax	4	
- Four vessel disease	5	
- Vertebrobasilary TIA	7	
- Retinal infarction	8	
- Symptomatic, but aspecific symtoms	9
- Contralateral symptomatic occlusion	10	
- retinal infarction	11	
- armclaudication due to occlusion subclavian artery, CEA needed for bypass	12	
- retinal infarction + TIAs	13	
- Ocular ischemic syndrome	14	
- ischemisch glaucoom	15	
- subclavian steal syndrome	16	
- TGA	17

We will group as follows in `Symptoms.5G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13
3. Stroke > 2, 3
4. Ocular > 4, 14, 15
5. Retinal infarction > 8, 11
6. Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 
3. Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17

We will also group as follows in `AsymptSympt2G`:

1. Asymptomatic > 0
2. TIA > 1, 7, 13 + Stroke > 2, 3 Ocular > 4, 14, 15 + Retinal infarction > 8, 11 + Other > 5, 9, 10, 12, 16, 17


```{r FixSymptoms, message=FALSE, warning=FALSE}
# Fix symptoms

attach(AEDB)

AEDB$sympt[is.na(AEDB$sympt)] <- -999

# Symptoms.5G
AEDB[,"Symptoms.5G"] <- NA
# AEDB$Symptoms.5G[sympt == "NA"] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == -999] <- NA
AEDB$Symptoms.5G[sympt == 0] <- "Asymptomatic"
AEDB$Symptoms.5G[sympt == 1 | sympt == 7 | sympt == 13] <- "TIA"
AEDB$Symptoms.5G[sympt == 2 | sympt == 3] <- "Stroke"
AEDB$Symptoms.5G[sympt == 4 | sympt == 14 | sympt == 15 ] <- "Ocular"
AEDB$Symptoms.5G[sympt == 8 | sympt == 11] <- "Retinal infarction"
AEDB$Symptoms.5G[sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Other"

# AsymptSympt
AEDB[,"AsymptSympt"] <- NA
AEDB$AsymptSympt[sympt == -999] <- NA
AEDB$AsymptSympt[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3] <- "Symptomatic"
AEDB$AsymptSympt[sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Ocular and others"

# AsymptSympt
AEDB[,"AsymptSympt2G"] <- NA
AEDB$AsymptSympt2G[sympt == -999] <- NA
AEDB$AsymptSympt2G[sympt == 0] <- "Asymptomatic"
AEDB$AsymptSympt2G[sympt == 1 | sympt == 7 | sympt == 13 | sympt == 2 | sympt == 3 | sympt == 4 | sympt == 14 | sympt == 15 | sympt == 8 | sympt == 11 | sympt == 5 | sympt == 9 | sympt == 10 | sympt == 12 | sympt == 16 | sympt == 17] <- "Symptomatic"

detach(AEDB)

# table(AEDB$sympt, useNA = "ifany")
# table(AEDB$AsymptSympt2G, useNA = "ifany")
# table(AEDB$Symptoms.5G, useNA = "ifany")
# 
# table(AEDB$AsymptSympt2G, AEDB$sympt, useNA = "ifany")
# table(AEDB$Symptoms.5G, AEDB$sympt, useNA = "ifany")
table(AEDB$AsymptSympt2G, AEDB$Symptoms.5G, useNA = "ifany")

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "sympt", "Symptoms.5G", "AsymptSympt"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# table(AEDB.temp$Symptoms.5G, AEDB.temp$AsymptSympt)
# 
# rm(AEDB.temp)

```

We will also fix the _plaquephenotypes_ variable.  

Coding of symptoms is as follows:

- missing	-999	
- not relevant -888
- fibrous	1	
- fibroatheromatous	2	
- atheromatous	3	


```{r FixPlaquePhenotypes, message=FALSE, warning=FALSE}

# Fix plaquephenotypes
attach(AEDB)
AEDB[,"OverallPlaquePhenotype"] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == -999] <- NA
AEDB$OverallPlaquePhenotype[plaquephenotype == 1] <- "fibrous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 2] <- "fibroatheromatous"
AEDB$OverallPlaquePhenotype[plaquephenotype == 3] <- "atheromatous"
detach(AEDB)

table(AEDB$OverallPlaquePhenotype)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "plaquephenotype", "OverallPlaquePhenotype"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```

We will also fix the _diabetes_ status variable. We define diabetes as history of a diagnosis and/or use of glucose-lowering medications.

```{r FixDiabetes, message=FALSE, warning=FALSE}
# Fix diabetes
attach(AEDB)
AEDB[,"DiabetesStatus"] <- NA
AEDB$DiabetesStatus[DM.composite == -999] <- NA
AEDB$DiabetesStatus[DM.composite == 0] <- "Control (no Diabetes Dx/Med)"
AEDB$DiabetesStatus[DM.composite == 1] <- "Diabetes"
detach(AEDB)

table(AEDB$DM.composite)

table(AEDB$DiabetesStatus)


# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)

```


We will also fix the _smoking_ status variable. We are interested in whether someone never, ever or is currently (at the time of inclusion) smoking. This is based on the questionnaire. 

- `diet801`: are you a smoker?
- `diet802`: did you smoke in the past?

We already have some variables indicating smoking status:

- `SmokingReported`: patient has reported to smoke.
- `SmokingYearOR`: smoking in the year of surgery?
- `SmokerCurrent`: currently smoking?



```{r FixSmoking, message=FALSE, warning=FALSE}
require(labelled)
AEDB$diet801 <- to_factor(AEDB$diet801)
AEDB$diet802 <- to_factor(AEDB$diet802)
AEDB$diet805 <- to_factor(AEDB$diet805)
AEDB$SmokingReported <- to_factor(AEDB$SmokingReported)
AEDB$SmokerCurrent <- to_factor(AEDB$SmokerCurrent)
AEDB$SmokingYearOR <- to_factor(AEDB$SmokingYearOR)

# table(AEDB$diet801)
# table(AEDB$diet802)
# table(AEDB$SmokingReported)
# table(AEDB$SmokerCurrent)
# table(AEDB$SmokingYearOR)
# table(AEDB$SmokingReported, AEDB$SmokerCurrent, useNA = "ifany", dnn = c("Reported smoking", "Current smoker"))
# 
# table(AEDB$diet801, AEDB$diet802, useNA = "ifany", dnn = c("Smoker", "Past smoker"))

cat("\nFixing smoking status.\n")
attach(AEDB)
AEDB[,"SmokerStatus"] <- NA
AEDB$SmokerStatus[diet802 == "don't know"] <- "Never smoked"
AEDB$SmokerStatus[diet802 == "I still smoke"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "no"] <- "Never smoked"
AEDB$SmokerStatus[SmokerCurrent == "no" & diet802 == "yes"] <- "Ex-smoker"
AEDB$SmokerStatus[SmokerCurrent == "yes"] <- "Current smoker"
AEDB$SmokerStatus[SmokerCurrent == "no data available/missing"] <- NA
# AEDB$SmokerStatus[is.na(SmokerCurrent)] <- "Never smoked"
detach(AEDB)

cat("\n* Current smoking status.\n")
table(AEDB$SmokerCurrent,
      useNA = "ifany", 
      dnn = c("Current smoker"))

cat("\n* Updated smoking status.\n")
table(AEDB$SmokerStatus,
      useNA = "ifany", 
      dnn = c("Updated smoking status"))

cat("\n* Comparing to 'SmokerCurrent'.\n")
table(AEDB$SmokerStatus, AEDB$SmokerCurrent, 
      useNA = "ifany", 
      dnn = c("Updated smoking status", "Current smoker"))

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "DM.composite", "DiabetesStatus"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$DiabetesStatus <- to_factor(AEDB.temp$DiabetesStatus)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix the _alcohol_ status variable.

```{r FixAlcohol, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"AlcoholUse"] <- NA
AEDB$AlcoholUse[diet810 == -999] <- NA
AEDB$AlcoholUse[diet810 == 0] <- "No"
AEDB$AlcoholUse[diet810 == 1] <- "Yes"
detach(AEDB)

table(AEDB$AlcoholUse)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```

We will also fix a history of CAD, stroke or peripheral intervention status variable. This will be based on `CAD_history`, `Stroke_history`, and `Peripheral.interv`

```{r FixCAD_History, message=FALSE, warning=FALSE}

# Fix diabetes
attach(AEDB)
AEDB[,"MedHx_CVD"] <- NA
AEDB$MedHx_CVD[CAD_history == 0 | Stroke_history == 0 | Peripheral.interv == 0] <- "No"
AEDB$MedHx_CVD[CAD_history == 1 | Stroke_history == 1 | Peripheral.interv == 1] <- "yes"
detach(AEDB)

table(AEDB$CAD_history)
table(AEDB$Stroke_history)
table(AEDB$Peripheral.interv)
table(AEDB$MedHx_CVD)

# AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", "diet810", "AlcoholUse"))
# require(labelled)
# AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
# AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
# AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)
# AEDB.temp$AlcoholUse <- to_factor(AEDB.temp$AlcoholUse)
# 
# DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)
# 
# rm(AEDB.temp)


```




# Athero-Express Biobank Study

## Baseline characteristics

We are interested in the following variables at baseline.

- Age (years)
- Female sex (N, %)
- Hypertension (N, %)
- SBP (mmHg)
- DBP (mmHg)
- Diabetes mellitus (N, %)
- Total cholesterol levels (mg/dL)
- LDL cholesterol levels (mg/dL)
- HDL cholesterol levels (mg/dL)
- Triglyceride levels (mg/dL)
- Use of statins (N, %)
- Use of antiplatelet drugs (N, %)
- BMI (kg/m²)
- Smoking status (N, %)
  - Never smokers
  - Ex-smokers
  - Current smokers
- History of CAD (N, %)
- History of PAD (N, %)
- Clinical manifestations
  - Asymptomatic
  - Amaurosis fugax
  - TIA
  - Stroke
- eGFR (mL/min/1.73 m²)
- MCP-1 plaque levels (pg/mL) (LUMINEX based, two experiments `MCP1`, and `MCP1_pg_ug_2015`)

> NOT AVAILABLE YET
- MCP-1 plasma levels (pg/mL) (OLINK based) 


```{r Baseline AEDB: creation, include = FALSE}
cat("====================================================================================================\n")
cat("SELECTION THE SHIZZLE\n")

### Artery levels
# AEdata$Artery_summary: 
#           value                                                                                   label
# NOT USE - 0 No artery known (yet), no surgery (patient ill, died, exited study), re-numbered to AAA
# USE - 1                                                                  carotid (left & right)
# USE - 2                                               femoral/iliac (left, right or both sides)
# NOT USE - 3                                               other carotid arteries (common, external)
# NOT USE - 4                                   carotid bypass and injury (left, right or both sides)
# NOT USE - 5                                                         aneurysmata (carotid & femoral)
# NOT USE - 6                                                                                   aorta
# NOT USE - 7                                            other arteries (renal, popliteal, vertebral)
# NOT USE - 8                        femoral bypass, angioseal and injury (left, right or both sides)

### AEdata$informedconsent
#           value                                                                                           label
# NOT USE - -999                                                                                         missing
# NOT USE - 0                                                                                        no, died
# USE - 1                                                                                             yes
# USE - 2                                                             yes, health treatment when possible
# USE - 3                                                                        yes, no health treatment
# USE - 4                                                yes, no health treatment, no commercial business
# NOT USE - 5                                                          yes, no tissue, no commerical business
# NOT USE - 6                      yes, no tissue, no questionnaires, no medical info, no commercial business
# USE - 7                             yes, no questionnaires, no health treatment, no commercial business
# USE - 8                                          yes, no questionnaires, health treatment when possible
# NOT USE - 9                  yes, no tissue, no questionnaires, no health treatment, no commerical business
# USE - 10                               yes, no health treatment, no medical info, no commercial business
# NOT USE - 11 yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business
# USE - 12                                                     yes, no questionnaires, no health treatment
# NOT USE - 13                                                             yes, no tissue, no health treatment
# NOT USE - 14                                                               yes, no tissue, no questionnaires
# NOT USE - 15                                                  yes, no tissue, health treatment when possible
# NOT USE - 16                                                                                  yes, no tissue
# USE - 17                                                                     yes, no commerical business
# USE - 18                                     yes, health treatment when possible, no commercial business
# USE - 19                                                    yes, no medical info, no commercial business
# USE - 20                                                                          yes, no questionnaires
# NOT USE - 21                         yes, no tissue, no questionnaires, no health treatment, no medical info
# NOT USE - 22                  yes, no tissue, no questionnaires, no health treatment, no commercial business
# USE - 23                                                                            yes, no medical info
# USE - 24                                                  yes, no questionnaires, no commercial business
# USE - 25                                    yes, no questionnaires, no health treatment, no medical info
# USE - 26                  yes, no questionnaires, health treatment when possible, no commercial business
# USE - 27                                                      yes,  no health treatment, no medical info
# NOT USE - 28                                                                             no, doesn't want to
# NOT USE - 29                                                                              no, unable to sign
# NOT USE - 30                                                                                 no, no reaction
# NOT USE - 31                                                                                        no, lost
# NOT USE - 32                                                                                     no, too old
# NOT USE - 34                                            yes, no medical info, health treatment when possible
# NOT USE - 35                                             no (never asked for IC because there was no tissue)
# USE - 36                    yes, no medical info, no commercial business, health treatment when possible
# NOT USE - 37                                                                                    no, endpoint
# USE - 38                                                         wil niets invullen, wel alles gebruiken
# USE - 39                                           second informed concents: yes, no commercial business
# NOT USE - 40                                                                              nooit geincludeerd

cat("- sanity checking PRIOR to selection")
library(data.table)
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
ae.hospital <- ifelse(AEDB$Hospital == 1, "Antonius", "UMCU")
table(ae.gender, ae.hospital, dnn = c("Sex", "Hospital"))
ae.gender <- ifelse(AEDB$Gender == 0, "Female", "Male")
table(ae.gender, AEDB$Artery_summary, dnn = c("Sex", "Artery"))
# table(ae.gender, AEDB$informedconsent, dnn = c("Sex", "IC"))

rm(ae.gender, ae.hospital)

# I change numeric and factors manually because, well, I wouldn't know how to fix it otherwise
# to have this 'tibble' work with 'tableone'... :-)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$diastoli <- as.numeric(AEDB$diastoli)
AEDB$systolic <- as.numeric(AEDB$systolic)

AEDB$TC_finalCU <- as.numeric(AEDB$TC_finalCU)
AEDB$LDL_finalCU <- as.numeric(AEDB$LDL_finalCU)
AEDB$HDL_finalCU <- as.numeric(AEDB$HDL_finalCU)
AEDB$TG_finalCU <- as.numeric(AEDB$TG_finalCU)

AEDB$TC_final <- as.numeric(AEDB$TC_final)
AEDB$LDL_final <- as.numeric(AEDB$LDL_final)
AEDB$HDL_final <- as.numeric(AEDB$HDL_final)
AEDB$TG_final <- as.numeric(AEDB$TG_final)

AEDB$Age <- as.numeric(AEDB$Age)
AEDB$GFR_MDRD <- as.numeric(AEDB$GFR_MDRD)
AEDB$BMI <- as.numeric(AEDB$BMI)
AEDB$eCigarettes <- as.numeric(AEDB$eCigarettes)
AEDB$ePackYearsSmoking <- as.numeric(AEDB$ePackYearsSmoking)
AEDB$EP_composite_time <- as.numeric(AEDB$EP_composite_time)

AEDB$macmean0 <- as.numeric(AEDB$macmean0)
AEDB$smcmean0 <- as.numeric(AEDB$smcmean0)
AEDB$neutrophils <- as.numeric(AEDB$neutrophils)
AEDB$Mast_cells_plaque <- as.numeric(AEDB$Mast_cells_plaque)
AEDB$vessel_density_averaged <- as.numeric(AEDB$vessel_density_averaged)

# IL6, IL6R, MCP1 measurements
AEDB$IL6 <- as.numeric(AEDB$IL6) # FACS plaque
AEDB$IL6_pg_ug_2015 <- as.numeric(AEDB$IL6_pg_ug_2015) # LUMINEX plaque
AEDB$IL6R_pg_ug_2015 <- as.numeric(AEDB$IL6R_pg_ug_2015) # LUMINEX plaque
AEDB$MCP1 <- as.numeric(AEDB$MCP1) # LUMINEX plaque
AEDB$MCP1_pg_ug_2015 <- as.numeric(AEDB$MCP1_pg_ug_2015) # LUMINEX plaque
AEDB$MCP1_pg_ml_2015 <- as.numeric(AEDB$MCP1_pg_ml_2015) # LUMINEX plaque
AEDB$hsCRP_plasma <- as.numeric(AEDB$hsCRP_plasma) # LUMINEX

AEDB$MCP1_plasma_olink <- as.numeric(AEDB$MCP1_plasma_olink) # OLINK plasma

require(labelled)
AEDB$ORyear <- to_factor(AEDB$ORyear)
AEDB$Gender <- to_factor(AEDB$Gender)
AEDB$Hospital <- to_factor(AEDB$Hospital)
AEDB$KDOQI <- to_factor(AEDB$KDOQI)
AEDB$BMI_WHO <- to_factor(AEDB$BMI_WHO)
AEDB$DiabetesStatus <- to_factor(AEDB$DiabetesStatus)
AEDB$SmokerStatus <- to_factor(AEDB$SmokerStatus)
AEDB$AlcoholUse <- to_factor(AEDB$AlcoholUse)

AEDB$Hypertension.selfreport <- to_factor(AEDB$Hypertension1)
AEDB$Hypertension.selfreportdrug <- to_factor(AEDB$Hypertension2)
AEDB$Hypertension.composite <- to_factor(AEDB$Hypertension.composite)
AEDB$Hypertension.drugs <- to_factor(AEDB$Hypertension.drugs)

AEDB$Med.anticoagulants <- to_factor(AEDB$Med.anticoagulants)
AEDB$Med.all.antiplatelet <- to_factor(AEDB$Med.all.antiplatelet)
AEDB$Med.Statin.LLD <- to_factor(AEDB$Med.Statin.LLD)

AEDB$Stroke_Dx <- to_factor(AEDB$Stroke_Dx)
AEDB$CAD_history <- to_factor(AEDB$CAD_history)
AEDB$PAOD <- to_factor(AEDB$PAOD)
AEDB$Peripheral.interv <- to_factor(AEDB$Peripheral.interv)
AEDB$MedHx_CVD <- to_factor(AEDB$MedHx_CVD)


AEDB$sympt <- to_factor(AEDB$sympt)
AEDB$Symptoms.3g <- to_factor(AEDB$Symptoms.3g)
AEDB$Symptoms.4g <- to_factor(AEDB$Symptoms.4g)
AEDB$Symptoms.5G <- to_factor(AEDB$Symptoms.5G)
AEDB$AsymptSympt <- to_factor(AEDB$AsymptSympt)
AEDB$AsymptSympt2G <- to_factor(AEDB$AsymptSympt2G)


AEDB$restenos <- to_factor(AEDB$restenos)
AEDB$stenose <- to_factor(AEDB$stenose)
AEDB$EP_composite <- to_factor(AEDB$EP_composite)
AEDB$Macrophages.bin <- to_factor(AEDB$Macrophages.bin)
AEDB$SMC.bin <- to_factor(AEDB$SMC.bin)
AEDB$IPH.bin <- to_factor(AEDB$IPH.bin)
AEDB$Calc.bin <- to_factor(AEDB$Calc.bin)
AEDB$Collagen.bin <- to_factor(AEDB$Collagen.bin)
AEDB$Fat.bin_10 <- to_factor(AEDB$Fat.bin_10)
AEDB$Fat.bin_40 <- to_factor(AEDB$Fat.bin_40)
AEDB$OverallPlaquePhenotype <- to_factor(AEDB$OverallPlaquePhenotype)

AEDB$Artery_summary <- to_factor(AEDB$Artery_summary)

AEDB$informedconsent <- to_factor(AEDB$informedconsent)

AEDB.CEA <- subset(AEDB,
                    (Artery_summary == "carotid (left & right)" | Artery_summary == "other carotid arteries (common, external)") & # we only want carotids
                       informedconsent != "missing" & # we are really strict in selecting based on 'informed consent'!
                       informedconsent != "no, died" &
                       informedconsent != "yes, no tissue, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commerical business" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info, no commercial business" &
                       informedconsent != "yes, no tissue, no health treatment" &
                       informedconsent != "yes, no tissue, no questionnaires" &
                       informedconsent != "yes, no tissue, health treatment when possible" &
                       informedconsent != "yes, no tissue" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no medical info" &
                       informedconsent != "yes, no tissue, no questionnaires, no health treatment, no commercial business" &
                       informedconsent != "no, doesn't want to" &
                       informedconsent != "no, unable to sign" &
                       informedconsent != "no, no reaction" &
                       informedconsent != "no, lost" &
                       informedconsent != "no, too old" &
                       informedconsent != "yes, no medical info, health treatment when possible" &
                       informedconsent != "no (never asked for IC because there was no tissue)" &
                       informedconsent != "no, endpoint" &
                       informedconsent != "nooit geincludeerd" & 
                     !is.na(AsymptSympt2G))
# AEDB.CEA[1:10, 1:10]
dim(AEDB.CEA)
```

```{r}
cat("===========================================================================================\n")
cat("CREATE BASELINE TABLE\n")

# Baseline table variables
basetable_vars = c("Hospital", "ORyear",
                   "Age", "Gender", 
                   "TC_finalCU", "LDL_finalCU", "HDL_finalCU", "TG_finalCU", 
                   "TC_final", "LDL_final", "HDL_final", "TG_final", 
                   "hsCRP_plasma",
                   "systolic", "diastoli", "GFR_MDRD", "BMI", 
                   "KDOQI", "BMI_WHO",
                   "SmokerStatus", "AlcoholUse",
                   "DiabetesStatus", 
                   "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                   "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                   "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                   "restenos", "stenose",
                   "MedHx_CVD", "CAD_history", "PAOD", "Peripheral.interv", 
                   "EP_composite", "EP_composite_time",
                   "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
                   "neutrophils", "Mast_cells_plaque",
                   "IPH.bin", "vessel_density_averaged",
                   "Calc.bin", "Collagen.bin", 
                   "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
                   "IL6", "IL6_pg_ug_2015", "IL6R_pg_ug_2015",
                   "MCP1", "MCP1_pg_ug_2015", "MCP1_pg_ml_2015",
                   "MCP1_plasma_olink")

basetable_bin = c("Gender", 
                  "KDOQI", "BMI_WHO",
                  "SmokerStatus", "AlcoholUse",
                  "DiabetesStatus", 
                  "Hypertension.selfreport", "Hypertension.selfreportdrug", "Hypertension.composite", "Hypertension.drugs", 
                  "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
                  "Stroke_Dx", "sympt", "Symptoms.5G", "AsymptSympt", "AsymptSympt2G",
                  "restenos", "stenose",
                  "CAD_history", "PAOD", "Peripheral.interv", 
                  "EP_composite", "Macrophages.bin", "SMC.bin",
                  "IPH.bin", 
                  "Calc.bin", "Collagen.bin", 
                  "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype")
# basetable_bin

basetable_con = basetable_vars[!basetable_vars %in% basetable_bin]
# basetable_con
```

### All patients
Showing the baseline table of the whole Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients

Showing the baseline table of the CEA patients in the Athero-Express Biobank.

```{r Baseline AEDB: Visualize AEDB CEA}
# Create baseline tables
# http://rstudio-pubs-static.s3.amazonaws.com/13321_da314633db924dc78986a850813a50d5.html
AEDB.CEA.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "Symptoms.4g",
                                         data = AEDB.CEA, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```

### CEA patients with `MCP1_pg_ug_2015`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ug_2015`.

```{r Baseline AEDB: Visualize subsetCEA}
AEDB.CEA.subset <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015))

AEDB.CEA.subset.AsymptSympt.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:6]
```

### CEA patients with `MCP1_pg_ug_2015` and `MCP1`

Showing the baseline table of the CEA patients in the Athero-Express Biobank with `MCP1_pg_ug_2015` _and_ `MCP1`.

```{r Baseline AEDB: Visualize subsetCEA with MCP1}

AEDB.CEA.subset.combo <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) | !is.na(MCP1))

AEDB.CEA.subset.combo.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.combo, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:6]
```

### CEA patients with plasma MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with plasma MCP1 levels. 

> Note plasma MCP1 was only measured in symptomatic patients.

```{r Baseline AEDB: Visualize subsetCEA with plasma MCP1}
AEDB.CEA.subset.plasma <- subset(AEDB.CEA, !is.na(MCP1_plasma_olink))

AEDB.CEA.subset.plasma.tableOne = print(CreateTableOne(vars = basetable_vars, 
                                         # factorVars = basetable_bin,
                                         # strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.plasma, includeNA = TRUE), 
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE, 
                          format = "pf", 
                          contDigits = 3)[,1:3]
```


### CEA patients with plasma _and_ plaque MCP1 levels

Showing the baseline table of the CEA patients in the Athero-Express Biobank with both plasma and plaque MCP1 levels.

> Note plasma MCP1 levels were only measured in symptomatic patients

```{r Baseline AEDB: Visualize subsetCEA with plasma MCP1 and plaque MCP1}
AEDB.CEA.subset.both <- subset(AEDB.CEA, !is.na(MCP1_pg_ug_2015) & !is.na(MCP1_plasma_olink))

AEDB.CEA.subset.both.tableOne = print(CreateTableOne(vars = basetable_vars,
                                         # factorVars = basetable_bin,
                                         # strata = "AsymptSympt2G",
                                         data = AEDB.CEA.subset.both, includeNA = TRUE),
                          nonnormal = c(), missing = TRUE,
                          quote = FALSE, noSpaces = FALSE, showAllLevels = TRUE, explain = TRUE,
                          format = "pf",
                          contDigits = 3)[,1:3]
```

Writing the baseline table to Excel format. 
```{r Baseline AEDB: write}
# Write basetable
require(openxlsx)

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.xlsx"),
           AEDB.CEA.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.wholeCEA.AsymptSympt.xlsx"),
           AEDB.CEA.subset.AsymptSympt.tableOne, 
           row.names = TRUE, 
           col.names = TRUE, 
           sheetName = "wholeAEDB_Baseline_Sympt")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEA.xlsx"),
           AEDB.CEA.subset.combo.tableOne,
           row.names = TRUE,
           col.names = TRUE,
           sheetName = "subsetAEDB_Baseline")

write.xlsx(file = paste0(BASELINE_loc, "/",Today,".",PROJECTNAME,".AE.BaselineTable.subsetCEAplasma.xlsx"),
           AEDB.CEA.subset.plasma.tableOne,
           row.names = TRUE,
           col.names = TRUE,
           sheetName = "subsetAEDB_Baseline_plasma")

```


## Data exploration

Here we inspect the data and when necessary transform quantitative measures. We will inspect the raw, natural log transformed + the smallest measurement, and inverse-normal transformation. 

### MCP1 plaque levels: experiment 2

We will explore the plaque levels. As noted above, we will use `MCP1_pg_ug_2015`, this was experiment 2 in 2015 on the LUMINEX-platform and measurements were corrected for total plaque protein content.

```{r DataExploration: MCP1 plaque Exp2}

summary(AEDB.CEA$MCP1_pg_ug_2015)

do.call(rbind , by(AEDB.CEA$MCP1_pg_ug_2015, AEDB.CEA$AsymptSympt2G, summary))


summary(AEDB.CEA$MCP1_pg_ml_2015)

do.call(rbind , by(AEDB.CEA$MCP1_pg_ml_2015, AEDB.CEA$AsymptSympt2G, summary))
```

```{r DataExploration: MCP1 plaque Exp2 visual}
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/ug", 
                    ggtheme = theme_minimal())

min_MCP1_pg_ug_2015 <- min(AEDB.CEA$MCP1_pg_ug_2015, na.rm = TRUE)
min_MCP1_pg_ug_2015

AEDB.CEA$MCP1_pg_ug_2015_LN <- log(AEDB.CEA$MCP1_pg_ug_2015 + min_MCP1_pg_ug_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    # title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_pg_ug_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ug_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ug_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ug_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)
```

We will explore the `MCP1_pg_ml_2015` levels and compare to the protein content corrected ones. 

```{r DataExploration: MCP1 plaque pg-ml Exp2 visual}
library(patchwork)
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_MCP1_pg_ml_2015 <- min(AEDB.CEA$MCP1_pg_ml_2015, na.rm = TRUE)
min_MCP1_pg_ml_2015

AEDB.CEA$MCP1_pg_ml_2015_LN <- log(AEDB.CEA$MCP1_pg_ml_2015 + min_MCP1_pg_ml_2015)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    # title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/mL", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_pg_ml_2015_rank <- qnorm((rank(AEDB.CEA$MCP1_pg_ml_2015, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_pg_ml_2015)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_pg_ml_2015_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/mL",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)
```

### MCP1 plaque levels: experiment 1

We will explore the plaque levels. As noted above, we will use `MCP1`, this was experiment 1 on the LUMINEX-platform and measurements were corrected for total plaque protein content.

```{r DataExploration: MCP1 plaque Exp1}

# summary(AEDB.CEA$MCP1)
# 
# do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
# 
attach(AEDB.CEA)
AEDB.CEA$MCP1[MCP1 == 0] <- NA
detach(AEDB.CEA)

summary(AEDB.CEA$MCP1)

do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 plaque Exp1 visual}
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "pg/mL", 
                    ggtheme = theme_minimal())

min_MCP1 <- min(AEDB.CEA$MCP1, na.rm = TRUE)
min_MCP1

AEDB.CEA$MCP1_LN <- log(AEDB.CEA$MCP1 + min_MCP1)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plaque levels",
                    xlab = "natural log-transformed pg/ug", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plaque levels",
                    xlab = "inverse-normal transformation pg/ug",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)

```

#### Correlations between MCP1 plaque levels and transformations

Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).
```{r MCP1 scatters: transformations}
p1 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "MCP1_rank", 
                        y = "MCP1_pg_ml_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "experiment 1",
                        ylab = "experiment 2",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p1 

p2 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "MCP1_rank", 
                        y = "MCP1_pg_ug_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "experiment 1",
                        ylab = "experiment 2",
                        title = "MCP1 plaque levels, INT, [pg/mL]/[pg/ug]",
                        ggtheme = theme_minimal())

p2 

p3 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "MCP1_pg_ml_2015_rank", 
                        y = "MCP1_pg_ug_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "experiment 2, [pg/mL]",
                        ylab = "experiment 2, [pg/ug]",
                        title = "MCP1 plaque levels, INT",
                        ggtheme = theme_minimal())

p3

```



### MCP1 plasma levels 

> Note plasma MCP1 levels were only measured in symptomatic patients.

We will explore the plasma levels. As noted above, we will use `MCP1`, this was measured in plasma on the OLINK-platform and measurements are given in arbitrary units.

```{r DataExploration: MCP1 OLINK}

# summary(AEDB.CEA$MCP1)
# 
# do.call(rbind , by(AEDB.CEA$MCP1, AEDB.CEA$AsymptSympt2G, summary))
# 
# attach(AEDB.CEA)
# AEDB.CEA$MCP1[MCP1 == 0] <- NA
# detach(AEDB.CEA)

summary(AEDB.CEA$MCP1_plasma_olink)

do.call(rbind , by(AEDB.CEA$MCP1_plasma_olink, AEDB.CEA$AsymptSympt2G, summary))

```

```{r DataExploration: MCP1 OLINK visual}
p1 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_plasma_olink", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plasma levels",
                    xlab = "AU", 
                    ggtheme = theme_minimal())

min_MCP1_plasma_olink <- min(AEDB.CEA$MCP1_plasma_olink, na.rm = TRUE)
min_MCP1_plasma_olink

AEDB.CEA$MCP1_plasma_olink_LN <- log(AEDB.CEA$MCP1_plasma_olink + min_MCP1_plasma_olink)
p2 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_plasma_olink_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    # add = "mean", 
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2), 
                    title = "MCP1 plasma levels",
                    xlab = "natural log-transformed AU", 
                    ggtheme = theme_minimal())

AEDB.CEA$MCP1_plasma_olink_rank <- qnorm((rank(AEDB.CEA$MCP1_plasma_olink, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1_plasma_olink)))
p3 <- ggpubr::gghistogram(AEDB.CEA, "MCP1_plasma_olink_rank",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = "MCP1 plasma levels",
                    xlab = "inverse-normal transformation AU",
                    ggtheme = theme_minimal())

p1 
p2 
p3
# ggpar(p1, legend = "") / ggpar(p2, legend = "")  | ggpar(p3, legend = "right")

rm(p1, p2, p3)

```

Based on the inverse-rank normal transformation we conclude there are no outliers and the data approximates a normal distribution. 
#### Correlations between MCP1 plaque and plasma levels

Here we compare the MCP1 plaque levels from experiment 1 and  2 (plaque) with plasma levels measured on the OLINK-platform. Given the arbitrary units used in the OLINK-method, we compare only inverse-normal transformed data.

```{r MCP1 scatters: transformations OLINK}
p1 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "MCP1_rank", 
                        y = "MCP1_pg_ml_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "plaque (experiment 1)",
                        ylab = "plaque (experiment 2)",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p1 

p2 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "MCP1_rank", 
                        y = "MCP1_plasma_olink_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "plaque (experiment 1)",
                        ylab = "plasma (OLINK)",
                        title = "MCP1 levels, INT, [pg/mL]/[AU]",
                        ggtheme = theme_minimal())

p2 

p3 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "MCP1_pg_ml_2015_rank", 
                        y = "MCP1_plasma_olink_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "plaque (experiment 2)",
                        ylab = "plasma (OLINK)",
                        title = "MCP1 levels, INT, [pg/mL]/[AU]",
                        ggtheme = theme_minimal())

p3

```



## Preliminary conclusion data exploration

In line with the previous work by [Marios Georgakis](https://www.ahajournals.org/doi/full/10.1161/CIRCRESAHA.119.315380){target="_blank"} we will apply _natural log transformation_ on all proteins and focus the analysis on MCP1 in plasma and plaque.

# Analyses

The analyses are focused on three elements: 

1) plaque vulnerability phenotypes
2) clinical status at inclusion (symptoms)
3) secondary clinical outcome during three (3) years of follow-up

## Covariates & other variables

1.  Age (continuous in 1-year increment). [`Age`]
2.  Sex (male vs. female). [`Gender`]
3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [`Hypertension.composite`]
4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes and/or administration of glucose lowering medication). [`DiabetesStatus`]
5.  Smoking (current, ex-, never). [`SmokerStatus`]
6.  LDL-C levels (continuous). [`LDL_final`]
7.  Use of lipid-lowering drugs. [`Med.Statin.LLD`]
8.  Use of antiplatelet drugs. [`Med.all.antiplatelet`]
9.  eGFR (continuous). [`GFR_MDRD`]
10.	BMI (continuous). [`BMI`]
11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [`MedHx_CVD`] combination of [`CAD_history`, `Stroke_history`, `Peripheral.interv`]
12.	Level of stenosis (50-70% vs. 70-99%). [`stenose`]
13. Year of surgery [`ORdate_year`] as we discovered in Van Lammeren _et al._ the composition of the plaque and therefore the Athero-Express Biobank Study has changed over the years. Likely through changes in lifestyle and primary prevention regimes.

## Models

We will analyze the data through four different models

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, and additionally adjusted for history hypertension (defined from medical history and/or use of antihypertensive medications), diabetes (defined as history of a diagnosis and/or use of glucose-lowering medications), current smoking, LDL-C levels at time of operation, use of statins, use of antiplatelet agents, eGFR, BMI, history of cardiovascular disease (coronary artery disease, stroke, peripheral artery disease), and level of stenosis (50-70%, 70-90%, 90-99%)

## A. Cross-sectional analysis plaque phenotypes

In the cross-sectional analysis of plaque and plasma MCP1, IL6, and IL6R levels we will focus on the following plaque vulnerability phenotypes:

- Percentage of macrophages (continuous trait)
- Percentage of SMCs (continuous trait)
- Number of intraplaque microvessels per 3-4 hotspots (continuous trait)
- Presence of moderate/heavy calcifications (binary trait)
- Presence of moderate/heavy collagen content (binary trait)
- Presence of lipid core no/<10% vs. >10% (binary trait)
- Presence of intraplaque hemorrhage (binary trait)

*Continous traits*
```{r CrossSec: plaques - transformations and visualisations continuous}

# macrophages
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_macmean <- min(AEDB.CEA$macmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % macrophages: ",min_macmean,".\n"))

AEDB.CEA$Macrophages_LN <- log(AEDB.CEA$macmean0 + min_macmean)

ggpubr::gghistogram(AEDB.CEA, "Macrophages_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$Macrophages_rank <- qnorm((rank(AEDB.CEA$macmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$macmean0)))
ggpubr::gghistogram(AEDB.CEA, "Macrophages_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% macrophages",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# smooth muscle cells
cat("Summary of data.\n")
summary(AEDB.CEA$macmean0)

min_smcmean <- min(AEDB.CEA$smcmean0, na.rm = TRUE)
cat(paste0("\nMinimum value % smooth muscle cells: ",min_smcmean,".\n"))

AEDB.CEA$SMC_LN <- log(AEDB.CEA$smcmean0 + min_smcmean)

ggpubr::gghistogram(AEDB.CEA, "SMC_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "natural log-transformed %", 
                    ggtheme = theme_minimal())

AEDB.CEA$SMC_rank <- qnorm((rank(AEDB.CEA$smcmean0, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$smcmean0)))
ggpubr::gghistogram(AEDB.CEA, "SMC_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "% smooth muscle cells",
                    xlab = "inverse-rank normalized %", 
                    ggtheme = theme_minimal())

# vessel density
cat("Summary of data.\n")
summary(AEDB.CEA$vessel_density_averaged)

min_vesseldensity <- min(AEDB.CEA$vessel_density_averaged, na.rm = TRUE)
min_vesseldensity
cat(paste0("\nMinimum value number of intraplaque neovessels per 3-4 hotspots: ",min_vesseldensity,".\n"))

AEDB.CEA$VesselDensity_LN <- log(AEDB.CEA$vessel_density_averaged + min_vesseldensity)

ggpubr::gghistogram(AEDB.CEA, "VesselDensity_LN", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                    xlab = "natural log-transformed number", 
                    ggtheme = theme_minimal())

AEDB.CEA$VesselDensity_rank <- qnorm((rank(AEDB.CEA$vessel_density_averaged, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$vessel_density_averaged)))
ggpubr::gghistogram(AEDB.CEA, "VesselDensity_rank", 
                    # y = "..count..", 
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"), 
                    add = "median", 
                    #add_density = TRUE,
                    rug = TRUE,
                    #add.params =  list(color = "black", linetype = 2), 
                    title = "number of intraplaque neovessels per 3-4 hotspots",
                   xlab = "inverse-rank normalized number", 
                    ggtheme = theme_minimal())
```

*Binary traits*
```{r CrossSec: plaques - transformations and visualisations binary}

# calcification
cat("Summary of data.\n")
summary(AEDB.CEA$Calc.bin)
contrasts(AEDB.CEA$Calc.bin)

AEDB.CEA$CalcificationPlaque <- as.factor(AEDB.CEA$Calc.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CalcificationPlaque)) %>%
  group_by(Gender, CalcificationPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CalcificationPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Calcification",
                    xlab = "calcification", 
                    ggtheme = theme_minimal())
rm(df)

# collagen
cat("Summary of data.\n")
summary(AEDB.CEA$Collagen.bin)
contrasts(AEDB.CEA$Collagen.bin)

AEDB.CEA$CollagenPlaque <- as.factor(AEDB.CEA$Collagen.bin)

df <- AEDB.CEA %>%
  filter(!is.na(CollagenPlaque)) %>%
  group_by(Gender, CollagenPlaque) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "CollagenPlaque", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Collagen",
                    xlab = "collagen", 
                    ggtheme = theme_minimal())
rm(df)

# fat 10%
cat("Summary of data.\n")
summary(AEDB.CEA$Fat.bin_10)
contrasts(AEDB.CEA$Fat.bin_10)

AEDB.CEA$Fat10Perc <- as.factor(AEDB.CEA$Fat.bin_10)

df <- AEDB.CEA %>%
  filter(!is.na(Fat10Perc)) %>%
  group_by(Gender, Fat10Perc) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "Fat10Perc", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque fat",
                    xlab = "intraplaque fat", 
                    ggtheme = theme_minimal())
rm(df)

# macrophages binned
cat("Summary of data.\n")
summary(AEDB.CEA$Macrophages.bin)
contrasts(AEDB.CEA$Macrophages.bin)

AEDB.CEA$MAC_binned <- as.factor(AEDB.CEA$Macrophages.bin)

df <- AEDB.CEA %>%
  filter(!is.na(MAC_binned)) %>%
  group_by(Gender, MAC_binned) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "MAC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (binned)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())
rm(df)

# macrophages grouped
cat("Summary of data.\n")
AEDB.CEA$macrophages <- as.factor(AEDB.CEA$macrophages)
summary(AEDB.CEA$macrophages)
contrasts(AEDB.CEA$macrophages)

AEDB.CEA$MAC_grouped <- as.factor(AEDB.CEA$macrophages)

df <- AEDB.CEA %>%
  filter(!is.na(MAC_grouped)) %>%
  group_by(Gender, MAC_grouped) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "MAC_grouped", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Macrophages (grouped)",
                    xlab = "Macrophages", 
                    ggtheme = theme_minimal())
rm(df)

# SMC binned
cat("Summary of data.\n")
summary(AEDB.CEA$SMC.bin)
contrasts(AEDB.CEA$SMC.bin)

AEDB.CEA$SMC_binned <- as.factor(AEDB.CEA$SMC.bin)

df <- AEDB.CEA %>%
  filter(!is.na(SMC_binned)) %>%
  group_by(Gender, SMC_binned) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "SMC_binned", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (binned)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())
rm(df)

# SMC grouped
cat("Summary of data.\n")
AEDB.CEA$smc <- as.factor(AEDB.CEA$smc)
summary(AEDB.CEA$smc)
contrasts(AEDB.CEA$smc)

AEDB.CEA$SMC_grouped <- as.factor(AEDB.CEA$smc)

df <- AEDB.CEA %>%
  filter(!is.na(SMC_grouped)) %>%
  group_by(Gender, SMC_grouped) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "SMC_grouped", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "SMC (grouped)",
                    xlab = "SMC", 
                    ggtheme = theme_minimal())
rm(df)


# IPH
cat("Summary of data.\n")
summary(AEDB.CEA$IPH.bin)
contrasts(AEDB.CEA$IPH.bin)

AEDB.CEA$IPH <- as.factor(AEDB.CEA$IPH.bin)

df <- AEDB.CEA %>%
  filter(!is.na(IPH)) %>%
  group_by(Gender, IPH) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "IPH", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Intraplaque hemorrhage",
                    xlab = "intraplaque hemorrhage", 
                    ggtheme = theme_minimal())
rm(df)

# Symptoms
cat("Summary of data.\n")
summary(AEDB.CEA$AsymptSympt)
contrasts(AEDB.CEA$AsymptSympt)

AEDB.CEA$AsymptSympt <- as.factor(AEDB.CEA$AsymptSympt)

df <- AEDB.CEA %>%
  filter(!is.na(AsymptSympt)) %>%
  group_by(Gender, AsymptSympt) %>%
summarise(counts = n()) 

ggpubr::ggbarplot(df, x = "AsymptSympt", y = "counts",
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#DB003F", "#1290D9"),
                    label = TRUE, lab.vjust = 2, lab.col = "#FFFFFF",
                    title = "Symptoms",
                    xlab = "symptoms", 
                    ggtheme = theme_minimal())
rm(df)

```

#### Correlations between MCP1 plaque levels and surgery year

Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).

```{r MCP1 scatters: year of surgery}
p1 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "experiment 1",
                        title = "MCP1 plaque levels, INT, [pg/mL]",
                        ggtheme = theme_minimal())

p1 

p2 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_pg_ml_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "experiment 2, [pg/mL]",
                        title = "MCP1 plaque levels, INT, [pg/mL]/[pg/ug]",
                        ggtheme = theme_minimal())

p2 

p3 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_pg_ug_2015_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "experiment 2, [pg/ug]",
                        title = "MCP1 plaque levels, INT",
                        ggtheme = theme_minimal())

p3

```

#### Correlations between MCP1 plasma levels and surgery year

Here we compare the MCP1 plaque levels from experiment 1 with those experiment 2. The latter we measured in pg/mL and also corrected for the total protein content (pg/ug).

```{r MCP1 plasma scatters: year of surgery}
library(patchwork)
p1 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_plasma_olink",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "MCP1 plasma - OLINK",
                        title = "MCP1 plasma, [AU]",
                        ggtheme = theme_minimal())

p2 <- ggpubr::ggscatter(AEDB.CEA, 
                        x = "ORyear", 
                        y = "MCP1_plasma_olink_rank",
                        color = "#1290D9",
                        # fill = "Gender",
                        # palette = c("#1290D9", "#DB003F"),
                        add = "reg.line",
                        add.params =  list(color = "black", linetype = 2),
                        cor.coef = TRUE, cor.method = "spearman",
                        xlab = "year of surgery",
                        ylab = "MCP1 plasma - OLINK",
                        title = "MCP1 plasma, INT, [AU]",
                        ggtheme = theme_minimal())

p1 / p2
rm(p1, p2)
```


In this section we make some variables to assist with analysis.
```{r CrossSec: plaques - setup regression }
AEDB.CEA.samplesize = nrow(AEDB.CEA)
# TRAITS.PROTEIN = c("IL6_LN", "MCP1_LN", "IL6_pg_ug_2015_LN", "IL6R_pg_ug_2015_LN", "MCP1_pg_ug_2015_LN")
# TRAITS.PROTEIN.RANK = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank")
# TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_rank")
TRAITS.PROTEIN.RANK = c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank", "MCP1_rank", "MCP1_plasma_olink_rank")

# TRAITS.CON = c("Macrophages_LN", "SMC_LN", "VesselDensity_LN")
# TRAITS.CON.RANK = c("Macrophages_rank")
TRAITS.CON.RANK = c("Macrophages_rank", "SMC_rank", "VesselDensity_rank")

# TRAITS.BIN = c("MAC_binned")
TRAITS.BIN = c("CalcificationPlaque", "CollagenPlaque", "Fat10Perc", "IPH",
               "MAC_binned", "SMC_binned")


# "Hospital", 
# "Age", "Gender", 
# "TC_final", "LDL_final", "HDL_final", "TG_final", 
# "systolic", "diastoli", "GFR_MDRD", "BMI", 
# "KDOQI", "BMI_WHO",
# "SmokerCurrent", "eCigarettes", "ePackYearsSmoking",
# "DiabetesStatus", "Hypertension.composite", 
# "Hypertension.drugs", "Med.anticoagulants", "Med.all.antiplatelet", "Med.Statin.LLD", 
# "Stroke_Dx", "sympt", "Symptoms.5G", "restenos",
# "EP_composite", "EP_composite_time",
# "macmean0", "smcmean0", "Macrophages.bin", "SMC.bin",
# "neutrophils", "Mast_cells_plaque",
# "IPH.bin", "vessel_density_averaged",
# "Calc.bin", "Collagen.bin", 
# "Fat.bin_10", "Fat.bin_40", "OverallPlaquePhenotype",
# "IL6_pg_ug_2015", "MCP1_pg_ug_2015", 
# "QC2018_FILTER", "CHIP", "SAMPLE_TYPE",
# "CAD_history", "Stroke_history", "Peripheral.interv",
# "stenose"

# 1.  Age (continuous in 1-year increment). [Age]
# 2.  Sex (male vs. female). [Gender]
# 3.  Presence of hypertension at baseline (defined either as history of hypertension, SBP ≥140 mm Hg, DBP ≥90 mm Hg, or prescription of antihypertensive medications). [Hypertension.composite]
# 4.  Presence of diabetes mellitus at baseline (defined either as a history of diabetes, administration of glucose lowering medication, HbA1c ≥6.5%, fasting glucose ≥126 mg/dl, .or random glucose levels ≥200 mg/dl). [DiabetesStatus]
# 5.  Smoking (current, ex-, never). [SmokerCurrent]
# 6.  LDL-C levels (continuous). [LDL_final]
# 7.  Use of lipid-lowering drugs. [Med.Statin.LLD]
# 8.  Use of antiplatelet drugs. [Med.all.antiplatelet]
# 9.  eGFR (continuous). [GFR_MDRD]
# 10.	BMI (continuous). [BMI]
# 11.	History of cardiovascular disease (stroke, coronary artery disease, peripheral artery disease). [MedHx_CVD] combinatino of: [CAD_history, Stroke_history, Peripheral.interv]
# 12.	Level of stenosis (50-70% vs. 70-99%). [stenose]

# Models 
# Model 1: adjusted for age and sex
# Model 2: adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis,

AEDB.CEA$ORdate_epoch <- as.numeric(AEDB.CEA$dateok)
AEDB.CEA$ORdate_year <- as.numeric(year(AEDB.CEA$dateok))

cat("Summary of 'year of surgery' in 'epoch' (); coded as `numeric()`\n")
summary(AEDB.CEA$ORdate_epoch)

cat("\nSummary of 'year of surgery' in 'years' (); coded as `factor()`\n")
table(AEDB.CEA$ORdate_year)

COVARIATES_M1 = c("Age", "Gender", "ORdate_year")
# COVARIATES_M1 = c("Age", "Gender", "ORdate_epoch")

COVARIATES_M2 = c(COVARIATES_M1,  
               "Hypertension.composite", "DiabetesStatus", 
               "SmokerStatus", 
               # "SmokerCurrent",
               "Med.Statin.LLD", "Med.all.antiplatelet", 
               "GFR_MDRD", "BMI", 
               # "CAD_history", "Stroke_history", "Peripheral.interv", 
               "MedHx_CVD",
               "stenose")

# COVARIATES_M3 = c(COVARIATES_M2, "LDL_final")

# COVARIATES_M4 = c(COVARIATES_M2, "hsCRP_plasma")

# COVARIATES_M5 = c(COVARIATES_M2, "IL6_pg_ug_2015_LN")
# COVARIATES_M5rank = c(COVARIATES_M2, "IL6_pg_ug_2015_rank")

```

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plasma/plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plasma/plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year,
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.PlaquePhenotypes.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

#### Quantitative plaque traits

Analysis of continuous/quantitative plaque traits as a function of plasma/plaque MCP1 levels.
```{r CrossSec: plaques - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.CON.RANK)) {
    TRAIT = TRAITS.CON.RANK[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

#### Binary plaque traits

Analysis of binary plaque traits as a function of plasma/plaque MCP1 levels.
```{r CrossSec: plaques - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(TRAITS.BIN)) {
    TRAIT = TRAITS.BIN[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.PlaquePhenotypes.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.PlaquePheno")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## B. Cross-sectional analysis symptoms

We will perform a cross-sectional analysis between plaque and plasma MCP1, IL6, and IL6R levels and the 'clinical status' of the plaque in terms of presence of patients' symptoms (symptomatic vs. asymptomatic). The symptoms of interest are:

- stroke
- TIA
- retinal infarction
- amaurosis fugax
- asymptomatic

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

> NOTE Given that MCP1 plasma was only measured in symptomatic patients, we exclude it from this analysis.

```{r CrossSec: symptoms - logistic regression MODEL1 RANK, include=TRUE, paged.print=TRUE}
TRAITS.PROTEIN.RANK.limit <- c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank", "MCP1_rank")
GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK.limit)) {
  PROTEIN = TRAITS.PROTEIN.RANK.limit[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
              data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Uni.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.

> NOTE Given that MCP1 plasma was only measured in symptomatic patients, we exclude it from this analysis.

```{r CrossSec: symptoms - logistic regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK.limit)) {
  PROTEIN = TRAITS.PROTEIN.RANK.limit[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "AsymptSympt"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- glm(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + 
                 Hypertension.composite + DiabetesStatus + SmokerStatus + 
                 Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                 MedHx_CVD + stenose, 
               data  =  currentDF, family = binomial(link = "logit"))
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 16, nrow = 0))
    GLM.results.TEMP[1,] = GLM.BIN(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "Z-value", "P-value", "r^2_l", "r^2_cs", "r^2_nagelkerke", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`Z-value` <- as.numeric(GLM.results$`Z-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2_l` <- as.numeric(GLM.results$`r^2_l`)
GLM.results$`r^2_cs` <- as.numeric(GLM.results$`r^2_cs`)
GLM.results$`r^2_nagelkerke` <- as.numeric(GLM.results$`r^2_nagelkerke`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

# Save the data
cat("Writing results to Excel-file...\n")

### Univariate
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Bin.Multi.Symptoms")

# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)

```


## C. Longitudinal analysis secondary clinical outcome

For the longitudinal analyses of plaque and plasma MCP1, IL6, and IL6R levels and secondary cardiovascular events over a three-year follow-up period. 

The _primary outcome_ is defined as "a composite of fatal or non-fatal myocardial infarction, fatal or non-fatal stroke, ruptured aortic aneurysm, fatal cardiac failure, coronary or peripheral interventions, leg amputation due to vascular causes, and cardiovascular death", i.e. major adverse cardiovascular events (MACE). Variable: `epmajor.3years`, these include:
- myocardial infarction (MI)
- cerebral infarction (CVA/stroke)
- cardiovascular death (exact cause to be investigated)
- cerebral bleeding (CVA/stroke)
- fatal myocardial infarction (MI)
- fatal cerebral infarction
- fatal cerebral bleeding
- sudden death
- fatal heart failure
- fatal aneurysm rupture
- other cardiovascular death..

The _secondary outcomes_ will be 

- incidence of fatal or non-fatal stroke (ischemic and bleeding) - variable: `epstroke.3years`, these include:
  - cerebral infarction (CVA/stroke)
  - cerebral bleeding (CVA/stroke)
  - fatal cerebral infarction
  - fatal cerebral bleeding.
- incidence of acute coronary events (fatal or non-fatal myocardial infarction, coronary interventions) - variable: `epcoronary.3years`, these include:
  - myocardial infarction (MI)
  - coronary angioplasty (PCI/PTCA)
  - cardiovascular death (exact cause to be investigated)
  - coronary bypass (CABG)
  - fatal myocardial infarction (MI)
  - sudden death.
- cardiovascular death - variable: `epcvdeath.3years`, these include:
  - cardiovascular death (exact cause to be investigated)
  - fatal myocardial infarction (MI)
  - fatal cerebral infarction
  - fatal cerebral bleeding
  - sudden death
  - fatal heart failure
  - fatal aneurysm rupture
  - other cardiovascular death..

### 30- and 90-days FU events

We will use 3-year follow-up, but we will also calculate 30 days and 90 days follow-up 'time-to-event' variables. On average there are 365.25 days in a year. We can calculate 30-days and 90-days follow-up time based on the three years follow-up. 

```{r Calculate new FU cut-offs: maximum FU}
cutt.off.30days = (1/365.25) * 30
cutt.off.90days = (1/365.25) * 90

# Fix maximum FU of 30 and 90 days
AEDB <- AEDB %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    FU.cutt.off.30days = ifelse(max.followup <= cutt.off.30days, max.followup, cutt.off.30days),
    FU.cutt.off.90days = ifelse(max.followup <= cutt.off.90days, max.followup, cutt.off.90days)
  ) 

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "max.followup", 
                                      "FU.cutt.off.3years",
                                      "FU.cutt.off.30days", 
                                      "FU.cutt.off.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)


```

Here we will calculate the new 30- and 90-days follow-up of the events and their event-times of interest:

- MACE (`epmajor.3years`)
- Stroke (`epstroke.3years`)
- Coronary events (`epcoronary.3years`)
- Cardiovascular death (`epcvdeath.3years`)


```{r Calculate new FU cut-offs: times}
avg_days_in_year = 365.25
cutt.off.30days.scaled <- cutt.off.30days * 365.25
cutt.off.90days.scaled <- cutt.off.90days * 365.25
# Event times
AEDB <- AEDB %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

AEDB.CEA <- AEDB.CEA %>%
  mutate(
    ep_major_t_30days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_stroke_t_30days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_coronary_t_30days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_cvdeath_t_30days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.30days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.30days.scaled),
    ep_major_t_90days = ifelse(ep_major_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                               ep_major_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_stroke_t_90days = ifelse(ep_stroke_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                ep_stroke_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_coronary_t_90days = ifelse(ep_coronary_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                  ep_coronary_t_3years * avg_days_in_year, cutt.off.90days.scaled),
    ep_cvdeath_t_90days = ifelse(ep_cvdeath_t_3years * avg_days_in_year <= cutt.off.90days.scaled, 
                                 ep_cvdeath_t_3years * avg_days_in_year, cutt.off.90days.scaled)
  ) 

```


```{r Cox-regressions: new times}

attach(AEDB)
AEDB[,"epmajor.30days"] <- AEDB$epmajor.3years
AEDB$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB[,"epstroke.30days"] <- AEDB$epstroke.3years
AEDB$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB[,"epcoronary.30days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB[,"epcvdeath.30days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB[,"epmajor.90days"] <- AEDB$epmajor.3years
AEDB$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB[,"epstroke.90days"] <- AEDB$epstroke.3years
AEDB$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB[,"epcoronary.90days"] <- AEDB$epcoronary.3years
AEDB$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB[,"epcvdeath.90days"] <- AEDB$epcvdeath.3years
AEDB$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB)

AEDB.temp <- subset(AEDB,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.temp$Gender <- to_factor(AEDB.temp$Gender)
AEDB.temp$Hospital <- to_factor(AEDB.temp$Hospital)
AEDB.temp$Artery_summary <- to_factor(AEDB.temp$Artery_summary)

DT::datatable(AEDB.temp[1:10,], caption = "Excerpt of the whole AEDB.", rownames = FALSE)

rm(AEDB.temp)

attach(AEDB.CEA)
AEDB.CEA[,"epmajor.30days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.30days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epstroke.30days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.30days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcoronary.30days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.30days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epcvdeath.30days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.30days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.30days] <- 0

AEDB.CEA[,"epmajor.90days"] <- AEDB.CEA$epmajor.3years
AEDB.CEA$epmajor.90days[epmajor.3years == 1 & ep_major_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epstroke.90days"] <- AEDB.CEA$epstroke.3years
AEDB.CEA$epstroke.90days[epstroke.3years == 1 & ep_stroke_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcoronary.90days"] <- AEDB.CEA$epcoronary.3years
AEDB.CEA$epcoronary.90days[epcoronary.3years == 1 & ep_coronary_t_3years > cutt.off.90days] <- 0

AEDB.CEA[,"epcvdeath.90days"] <- AEDB.CEA$epcvdeath.3years
AEDB.CEA$epcvdeath.90days[epcvdeath.3years == 1 & ep_cvdeath_t_3years > cutt.off.90days] <- 0

detach(AEDB.CEA)

AEDB.CEA.temp <- subset(AEDB.CEA,  select = c("STUDY_NUMBER", "UPID", "Age", "Gender", "Hospital", "Artery_summary", 
                                      "epmajor.3years", "epstroke.3years", "epcoronary.3years", "epcvdeath.3years",
                                      "epmajor.30days", "epstroke.30days", "epcoronary.30days", "epcvdeath.30days",
                                      "epmajor.90days", "epstroke.90days", "epcoronary.90days", "epcvdeath.90days"))
require(labelled)
AEDB.CEA.temp$Gender <- to_factor(AEDB.CEA.temp$Gender)
AEDB.CEA.temp$Hospital <- to_factor(AEDB.CEA.temp$Hospital)
AEDB.CEA.temp$Artery_summary <- to_factor(AEDB.CEA.temp$Artery_summary)

DT::datatable(AEDB.CEA.temp[1:10,], caption = "Excerpt of the whole AEDB.CEA.", rownames = FALSE)

rm(AEDB.CEA.temp)



```

### Sanity checks

First we do some sanity checks and inventory the time-to-event and event variables.
```{r Cox-regressions: General}
# Reference: https://bioconductor.org/packages/devel/bioc/vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html
# If you want to suppress warnings and messages when installing/loading packages
# suppressPackageStartupMessages({})
install.packages.auto("survival")
install.packages.auto("survminer")
install.packages.auto("Hmisc")

cat("* Creating function to summarize Cox regression and prepare container for results.")
# Function to get summary statistics from Cox regression model
COX.STAT <- function(coxfit, DATASET, OUTCOME, protein){
  cat("Summarizing Cox regression results for '", protein ,"' and its association to '",OUTCOME,"' in '",DATASET,"'.\n")
  if (nrow(summary(coxfit)$coefficients) == 1) {
    output = c(protein, rep(NA,8))
    cat("Model not fitted; probably singular.\n")
  }else {
    cat("Collecting data.\n\n")
    cox.sum <- summary(coxfit)
    cox.effectsize = cox.sum$coefficients[1,1]
    cox.SE = cox.sum$coefficients[1,3]
    cox.HReffect = cox.sum$coefficients[1,2]
    cox.CI_low = exp(cox.effectsize - 1.96 * cox.SE)
    cox.CI_up = exp(cox.effectsize + 1.96 * cox.SE)
    cox.zvalue = cox.sum$coefficients[1,4]
    cox.pvalue = cox.sum$coefficients[1,5]
    cox.sample_size = cox.sum$n
    cox.nevents = cox.sum$nevent
    
    output = c(DATASET, OUTCOME, protein, cox.effectsize, cox.SE, cox.HReffect, cox.CI_low, cox.CI_up, cox.zvalue, cox.pvalue, cox.sample_size, cox.nevents)
    cat("We have collected the following:\n")
    cat("Dataset used..............:", DATASET, "\n")
    cat("Outcome analyzed..........:", OUTCOME, "\n")
    cat("Protein...................:", protein, "\n")
    cat("Effect size...............:", round(cox.effectsize, 6), "\n")
    cat("Standard error............:", round(cox.SE, 6), "\n")
    cat("Odds ratio (effect size)..:", round(cox.HReffect, 3), "\n")
    cat("Lower 95% CI..............:", round(cox.CI_low, 3), "\n")
    cat("Upper 95% CI..............:", round(cox.CI_up, 3), "\n")
    cat("T-value...................:", round(cox.zvalue, 6), "\n")
    cat("P-value...................:", signif(cox.pvalue, 8), "\n")
    cat("Sample size in model......:", cox.sample_size, "\n")
    cat("Number of events..........:", cox.nevents, "\n")
  }
  return(output)
  print(output)
} 

times = c("ep_major_t_3years", 
          "ep_stroke_t_3years", "ep_coronary_t_3years", "ep_cvdeath_t_3years")

endpoints = c("epmajor.3years", 
              "epstroke.3years", "epcoronary.3years", "epcvdeath.3years")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints){
  require(labelled)
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "year", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPerYear.",eventtime,".pdf"), plot = last_plot())
}

times30 = c("ep_major_t_30days", 
          "ep_stroke_t_30days", "ep_coronary_t_30days", "ep_cvdeath_t_30days")

endpoints30 = c("epmajor.30days", 
              "epstroke.30days", "epcoronary.30days", "epcvdeath.30days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints30){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times30){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times30){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer30Days.",eventtime,".pdf"), plot = last_plot())
}

times90 = c("ep_major_t_90days", 
          "ep_stroke_t_90days", "ep_coronary_t_90days", "ep_cvdeath_t_90days")

endpoints90 = c("epmajor.90days", 
              "epstroke.90days", "epcoronary.90days", "epcvdeath.90days")

cat("* Check the cases per event type - for sanity.")
for (events in endpoints90){
  print(paste0("Printing the summary of: ",events))
  # print(summary(AEDB.CEA[,events]))
  print(table(AEDB.CEA[,events]))
}

cat("* Check distribution of events over time - for sanity.")
for (eventtimes in times90){
  print(paste0("Printing the summary of: ",eventtimes))
  print(summary(AEDB.CEA[,eventtimes]))
}

for (eventtime in times90){
  
  print(paste0("Printing the distribution of: ",eventtime))
  p <- gghistogram(AEDB.CEA, x = eventtime, y = "..count..",
              main = eventtime, bins = 15, 
              xlab = "days", color = uithof_color[16], fill = uithof_color[16], ggtheme = theme_minimal()) 
 print(p)
 ggsave(file = paste0(QC_loc, "/",Today,".AEDB.CEA.EventDistributionPer90Days.",eventtime,".pdf"), plot = last_plot())
}


```


### Cox regressions

Let's perform the actual Cox-regressions. We will apply a couple of models: 

- Model 1: adjusted for age, sex, and year of surgery
- Model 2: adjusted for age, sex, year of surgery, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis

#### 3 years follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times)){
  eptime = times[i]
  ep = endpoints[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         # ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [years]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 30-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.30days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 30 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times30)){
  eptime = times30[i]
  ep = endpoints30[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".30days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.30days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.30days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```


#### 90-days follow-up

*MODEL 1*
```{r Cox-regression Analysis: Simple model, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 1 (Simple model)
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age+Gender + ORdate_year, data = TEMP.DF)

    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL1.90days.pdf"), height = 12, width = 10, onefile = TRUE)
    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
library(openxlsx)
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL1.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
#rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

#rm(head.style)

```

*MODEL 2*
```{r Cox-regression Analysis: MODEL 2, 90 days}
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))

# Looping over each protein/endpoint/time combination
for (i in 1:length(times90)){
  eptime = times90[i]
  ep = endpoints90[i]
  cat(paste0("* Analyzing the effect of plaque proteins on [",ep,"].\n"))
  cat(" - creating temporary SE for this work.\n")
  TEMP.DF = as.data.frame(AEDB.CEA)
  cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
  TEMP.DF$event <- as.integer(TEMP.DF[,ep])
  #as.integer(TEMP.DF[,ep] == "Excluded")

  TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
  cat(" - making strata of each of the plaque proteins and start survival analysis.\n")
  
  for (protein in 1:length(TRAITS.PROTEIN.RANK)){
    cat(paste0("   > processing [",TRAITS.PROTEIN.RANK[protein],"]; ",protein," out of ",length(TRAITS.PROTEIN.RANK)," proteins.\n"))
    # splitting into two groups
    TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]] <- cut2(TEMP.DF[,TRAITS.PROTEIN.RANK[protein]], g = 2)
    cat(paste0("   > cross tabulation of ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    show(table(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]))
    
    cat(paste0("\n   > fitting the model for ",TRAITS.PROTEIN.RANK[protein],"-stratum.\n"))
    fit <- survfit(as.formula(paste0("y ~ ", TRAITS.PROTEIN.RANK[protein])), data = TEMP.DF)
    
    cat(paste0("\n   > make a Kaplan-Meier-shizzle...\n"))
    # make Kaplan-Meier curve and save it
    show(ggsurvplot(fit, data = TEMP.DF,
                    palette = c("#DB003F", "#1290D9"),
                    # palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
                    linetype = c(1,2),
                    ylim = c(0.75, 1),
                    # linetype = c(1,2,3,4),
                    # conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
                    pval = FALSE, pval.method = FALSE, pval.size = 4,
                    risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
                    censor = FALSE,
                    legend = "right",
                    legend.title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
                    legend.labs = c("low", "high"),
                    title = paste0("Risk of ",ep,""), xlab = "Time [days]", font.main = c(16, "bold", "black")))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.survival.",ep,".2G.",
                               TRAITS.PROTEIN.RANK[protein],".90days.pdf"), width = 12, height = 10, onefile = FALSE)

    cat(paste0("\n   > perform the Cox-regression fashizzle and plot it...\n"))
    ### Do Cox-regression and plot it
    
    ### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
    cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
    coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.PROTEIN.RANK[protein] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)

  
    plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
         ylim = c(0.75, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         # ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
         lty = c(1,2), lwd = 2,
         ylab = "Suvival probability", xlab = "FU time [days]",
         mark.time = FALSE, axes = FALSE, bty = "n")
    legend("topright",
           c("low", "high"),
           title = paste0("",TRAITS.PROTEIN.RANK[protein],""),
           col = c("#DB003F", "#1290D9"),
           lty = c(1,2), lwd = 2,
           bty = "n")
    axis(side = 1, at = seq(0, 3, by = 1))
    axis(side = 2, at = seq(0, 1, by = 0.2))
    dev.copy2pdf(file = paste0(COX_loc,"/",
                               Today,".AEDB.CEA.Cox.",ep,".2G.",
                               # Today,".AEDB.CEA.Cox.",ep,".4G.",
                               TRAITS.PROTEIN.RANK[protein],".MODEL2.90days.pdf"), height = 12, width = 10, onefile = TRUE)

    show(summary(cox))

    cat(paste0("\n   > writing the Cox-regression fashizzle to Excel...\n"))

    COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
    COX.results.TEMP[1,] = COX.STAT(cox, "AEDB.CEA", ep, TRAITS.PROTEIN.RANK[protein])
    COX.results = rbind(COX.results, COX.results.TEMP)

  }
}

cat("- Edit the column names...\n")
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
                          "Beta", "s.e.m.",
                          "HR", "low95CI", "up95CI",
                          "Z-value", "P-value", "SampleSize", "N_events")

cat("- Correct the variable types...\n")
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)

AEDB.CEA.COX.results <- COX.results

# Save the data
cat("- Writing results to Excel-file...\n")
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AEDB.CEA.COX.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Cox.2G.MODEL2.90days.xlsx"),
           creator = "Sander W. van der Laan",
           sheetName = "Results", headerStyle = head.style,
           row.names = FALSE, col.names = TRUE, overwrite = TRUE)

# Removing intermediates
cat("- Removing intermediate files...\n")
rm(TEMP.DF, protein, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AEDB.CEA.COX.results)

rm(head.style)

```



# Correlations
We correlated plasma and plaque levels of the biomarkers.

## MCP1 plaque levels

```{r CrossSampleType Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("IL6_rank", "MCP1_rank", "IL6_pg_ug_2015_rank", "MCP1_pg_ug_2015_rank", "IL6R_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
# AEDB.CEA.temp <- subset(AEDB.CEA, 
#                           select = c("MCP1_rank", "MCP1_pg_ug_2015_rank",
#                                                TRAITS.BIN, TRAITS.CON.RANK)
#                                     )
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_pg_ug_2015_rank",
                                     TRAITS.BIN, 
                                     TRAITS.CON.RANK,
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )


AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$MAC_binned <- as.numeric(AEDB.CEA.temp$MAC_binned)
AEDB.CEA.temp$SMC_binned <- as.numeric(AEDB.CEA.temp$SMC_binned)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

```

```{r MCP1 Correlations table}
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}

corr_biomarkers.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank))
DT::datatable(corr_biomarkers.rank.df)

```

```{r MCP1 Correlations alternative visual 1}
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}


chart.Correlation.new(AEDB.CEA.matrix.RANK, method = "spearman", histogram = TRUE, pch = 3)
```


```{r MCP1 Correlations alternative visual 2}
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:

# ggpairs(AEDB.CEA, 
#         columns = c("MCP1_rank", "MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK), 
#         columnLabels = c("MCP1 (plasma)", "MCP1", 
#                          "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages", "SMC", "Vessel density"),
#         method = c("spearman"),
#         # ggplot2::aes(colour = Gender),
#         progress = FALSE) 

ggpairs(AEDB.CEA,
        columns = c("MCP1_pg_ug_2015_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"),
        columnLabels = c("MCP1",
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE)

```

## Circulating MCP1

Finally, we explored in a sub-sample, where circulating MCP-1 levels are available, the following:

1. A correlation between MCP-1 levels in the plaque and circulating MCP-1 levels
2. Associations of circulating MCP-1 levels with plaque vulnerability characteristics
3. Associations of circulating MCP-1 levels with the status of the plaque in terms of presence of symptoms (symptomatic vs. asymptomatic)
4. Associations of circulating MCP-1 levels with the primary composite endpoint of secondary cardiovascular events.

```{r MCP1 plasma Correlations}

# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)


# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c("MCP1_plasma_olink_rank", 
                                     TRAITS.BIN,
                                     TRAITS.CON.RANK, 
                                     "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite")
                                    )

AEDB.CEA.temp$CalcificationPlaque <- as.numeric(AEDB.CEA.temp$CalcificationPlaque)
AEDB.CEA.temp$CollagenPlaque <- as.numeric(AEDB.CEA.temp$CollagenPlaque)
AEDB.CEA.temp$Fat10Perc <- as.numeric(AEDB.CEA.temp$Fat10Perc)
AEDB.CEA.temp$MAC_binned <- as.numeric(AEDB.CEA.temp$MAC_binned)
AEDB.CEA.temp$SMC_binned <- as.numeric(AEDB.CEA.temp$SMC_binned)
AEDB.CEA.temp$IPH <- as.numeric(AEDB.CEA.temp$IPH)
AEDB.CEA.temp$Symptoms.5G <- as.numeric(AEDB.CEA.temp$Symptoms.5G)
AEDB.CEA.temp$AsymptSympt <- as.numeric(AEDB.CEA.temp$AsymptSympt)
AEDB.CEA.temp$EP_major <- as.numeric(AEDB.CEA.temp$EP_major)
AEDB.CEA.temp$EP_composite <- as.numeric(AEDB.CEA.temp$EP_composite)
# str(AEDB.CEA.temp)
AEDB.CEA.matrix.plasma.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers_plasma.rank <- round(cor(AEDB.CEA.matrix.plasma.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

corr_biomarkers_plasma_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.plasma.RANK, use = "pairwise.complete.obs", method = "spearman")

# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
ggcorrplot(corr_biomarkers_plasma.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           # p.mat = corr_biomarkers_plasma_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))

```

```{r MCP1 plasma Correlations table}
# flattenCorrMatrix
# --------------------------------
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    biomarker_row = rownames(cormat)[row(cormat)[ut]],
    biomarker_column = rownames(cormat)[col(cormat)[ut]],
    spearman_cor  =(cormat)[ut],
    pval = pmat[ut]
    )
}


corr_biomarkers_plasma.rank.df <- as.data.table(flattenCorrMatrix(corr_biomarkers_plasma.rank, corr_biomarkers_plasma_p.rank))
DT::datatable(corr_biomarkers_plasma.rank.df)
```

```{r MCP1 plasma Correlations alternative visual 1}
# chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("PerformanceAnalytics")
chart.Correlation.new <- function (R, histogram = TRUE, method = c("pearson", "kendall", 
    "spearman"), ...) 
{
    x = checkData(R, method = "matrix")
    if (missing(method)) 
        method = method[1]
    cormeth <- method
    panel.cor <- function(x, y, digits = 2, prefix = "", use = "pairwise.complete.obs", 
        method = cormeth, cex.cor, ...) {
        usr <- par("usr")
        on.exit(par(usr))
        par(usr = c(0, 1, 0, 1))
        r <- cor(x, y, use = use, method = method)
        txt <- format(c(r, 0.123456789), digits = digits)[1]
        txt <- paste(prefix, txt, sep = "")
        if (missing(cex.cor)) 
            cex <- 0.8/strwidth(txt)
        test <- cor.test(as.numeric(x), as.numeric(y), method = method)
        Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
            cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1), symbols = c("***", 
                "**", "*", ".", " "))
        text(0.5, 0.5, txt, cex = cex * (abs(r) + 0.3)/1.3)
        text(0.8, 0.8, Signif, cex = cex, col = 2)
    }
    f <- function(t) {
        dnorm(t, mean = mean(x), sd = sd.xts(x))
    }
    dotargs <- list(...)
    dotargs$method <- NULL
    rm(method)
    hist.panel = function(x, ... = NULL) {
        par(new = TRUE)
        hist(x, col = "#1290D9", probability = TRUE, axes = FALSE, 
        # hist(x, col = "light gray", probability = TRUE, axes = FALSE, 
            main = "", breaks = "FD")
        lines(density(x, na.rm = TRUE), col = "#E55738", lwd = 1)
        rug(x)
    }
    if (histogram) 
        pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, 
            diag.panel = hist.panel, ...)
    else pairs(x, gap = 0, lower.panel = panel.smooth, upper.panel = panel.cor, ...)
}

chart.Correlation.new(AEDB.CEA.matrix.plasma.RANK, method = "spearman", histogram = TRUE, pch = 3)
```


```{r MCP1 plasma Correlations alternative visual 2}
# alternative chart of a correlation matrix
# --------------------------------
# Alternative solution https://www.r-graph-gallery.com/199-correlation-matrix-with-ggally.html
install.packages.auto("GGally")

# Quick display of two cabapilities of GGally, to assess the distribution and correlation of variables 
library(GGally)
 
# From the help page:
ggpairs(AEDB.CEA,
        columns = c("MCP1_rank", TRAITS.BIN, TRAITS.CON.RANK, "Symptoms.5G", "AsymptSympt", "EP_major", "EP_composite"), 
        columnLabels = c("MCP1 (plasma)", 
                         "Calcification", "Collagen", "Fat 10%", "IPH", "Macrophages (binned)", "SMC (binned)", "Macrophages", "SMC", "Vessel density",
                         "Symptoms", "Symptoms (grouped)", "MACE", "Composite"),
        method = c("spearman"),
        # ggplot2::aes(colour = Gender),
        progress = FALSE) 
```

# Additional figures

## Age and sex
We want to create per-age-group figures. 

- Box and Whisker plot for MCP-1 plaque levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Box and Whisker plot for MCP-1 plasma levels by sex and age group (<55, 55-64, 65-74, 75-84, 85+)
- Scatter plot of the correlation and regression line between MCP-1 levels in plaque (y axis) and plasma (x axis).


```{r AgeGroups}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroup = factor(case_when(Age < 55 ~ "<55",
                                                     Age >= 55  & Age <= 64 ~ "55-64",
                                                     Age >= 65  & Age <= 74 ~ "65-74",
                                                     Age >= 75  & Age <= 84 ~ "75-84",
                                                     Age >= 85 ~ "85+"))) 

AEDB.CEA <- AEDB.CEA %>% mutate(AgeGroupSex = factor(case_when(Age < 55 & Gender == "male" ~ "<55 males" ,
                                                        Age >= 55  & Age <= 64 & Gender == "male"~ "55-64 males",
                                                        Age >= 65  & Age <= 74 & Gender == "male"~ "65-74 males",
                                                        Age >= 75  & Age <= 84 & Gender == "male"~ "75-84 males",
                                                        Age >= 85 & Gender == "male"~ "85+ males",
                                                        Age < 55 & Gender == "female" ~ "<55 females" ,
                                                        Age >= 55  & Age <= 64 & Gender == "female"~ "55-64 females ",
                                                        Age >= 65  & Age <= 74 & Gender == "female"~ "65-74 females",
                                                        Age >= 75  & Age <= 84 & Gender == "female"~ "75-84 females",
                                                        Age >= 85 & Gender == "female"~ "85+ females")))

table(AEDB.CEA$AgeGroup, AEDB.CEA$Gender)
table(AEDB.CEA$AgeGroupSex)

```

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per AgeGroup per Sex, ranked}

# ?ggpubr::ggboxplot()

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Age groups (years)",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "AgeGroup",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AgeGroup.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AgeGroup_perGender.pdf"), plot = last_plot())
```

#### MCP1 plasma levels

```{r MCP1 per AgeGroup per Sex, ranked plasma}
# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_plasma_olink_rank",
                  xlab = "Age groups (years)",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "AgeGroup",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.AgeGroup.pdf"), plot = last_plot())
# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA,
                  x = c("AgeGroup"),
                  y = "MCP1_plasma_olink_rank",
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.AgeGroup_perGender.pdf"), plot = last_plot())

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per AgeGroup per Sex}

# ?ggpubr::ggboxplot()
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Age groups (years)",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "AgeGroup",
                  palette = "npg",
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.AgeGroup.pdf"), plot = last_plot())

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.AgeGroup_perGender.pdf"), plot = last_plot())
```

### MCP1 plasma levels


```{r MCP1 per AgeGroup per Sex, plasma}
ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_plasma_olink", 
                  xlab = "Age groups (years)",
                  ylab = "MCP1 plasma [AU]",
                  color = "AgeGroup",
                  palette = "npg",
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.AgeGroup.pdf"), plot = last_plot())

ggpubr::ggboxplot(AEDB.CEA, 
                  x = c("AgeGroup"),
                  y = "MCP1_plasma_olink", 
                  xlab = "Age groups (years) per gender",
                  ylab = "MCP1 plasma [AU]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.AgeGroup_perGender.pdf"), plot = last_plot())
```


## Hypertension & blood pressure
We want to create figures of MCP1 levels stratified by hypertension/blood pressure, and use of anti-hypertensive drugs. 

- Box and Whisker plot for MCP-1 plaque levels by hypertension group (no, yes)
- Box and Whisker plot for MCP-1 plasma levels by systolic blood pressure group (<120, 120-139, 140-159,160+)


```{r BloodPressure}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(SBPGroup = factor(case_when(systolic < 120 ~ "<120",
                                                     systolic >= 120  & systolic <= 139 ~ "120-139",
                                                     systolic >= 140  & systolic <= 159 ~ "140-159",
                                                     systolic >= 160 ~ "160+"))) 

table(AEDB.CEA$SBPGroup, AEDB.CEA$Gender)

```

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and hypertension/blood pressure group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per BloodPressure per Sex, ranked}

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.SBPGroup.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypertension.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.HypertensionDrugs.pdf"), plot = last_plot())


```

#### MCP1 plasma levels


```{r MCP1 per BloodPressure per Sex, ranked plasma}

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)),
                  x = c("SBPGroup"),
                  y = "MCP1_plasma_olink_rank",
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") 
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.SBPGroup.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Hypertension.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.HypertensionDrugs.pdf"), plot = last_plot())

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per BloodPressure per Sex}

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.SBPGroup.pdf"), plot = last_plot())

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Hypertension.pdf"), plot = last_plot())

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") 
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.HypertensionDrugs.pdf"), plot = last_plot())

```

### MCP1 plasma levels

```{r MCP1 per BloodPressure per Sex, plasma}

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SBPGroup)), 
                  x = c("SBPGroup"),
                  y = "MCP1_plasma_olink", 
                  xlab = "Systolic blood pressure (mmHg) per gender",
                  ylab = "MCP1 plasma [AU]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.SBPGroup.pdf"), plot = last_plot())

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_plasma_olink", 
                  xlab = "Self-reported hypertension per gender",
                  ylab = "MCP1 plasma [AU]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Hypertension.pdf"), plot = last_plot())

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(Hypertension.selfreport) & !is.na(Hypertension.drugs)), 
                  x = c("Hypertension.selfreport"),
                  y = "MCP1_plasma_olink", 
                  xlab = "Self-reported hypertension per medication use",
                  ylab = "MCP1 plasma [AU]",
                  color = "Hypertension.drugs",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") 
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.HypertensionDrugs.pdf"), plot = last_plot())

```


## Hypercholesterolemia & LDL levels
We want to create figures of MCP1 levels stratified by hypercholesterolemia/LDL-levels, and use of lipid-lowering drugs. 

- Box and Whisker plot for MCP-1 plaque levels by hypercholesterolemia group (no, yes)
- Box and Whisker plot for MCP-1 plasma levels by LDL-levels (mmol/L) group (<100, 100-129, 130-159, 160-189, 190+)

```{r Hypercholesterolemia}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(LDLGroup = factor(case_when(LDL_finalCU < 100 ~ "<100",
                                                     LDL_finalCU >= 100  & LDL_finalCU <= 129 ~ "100-129",
                                                     LDL_finalCU >= 130  & LDL_finalCU <= 159 ~ "130-159",
                                                     LDL_finalCU >= 160  & LDL_finalCU <= 189 ~ "160-189",
                                                     LDL_finalCU >= 190 ~ "190+"))) 


table(AEDB.CEA$LDLGroup, AEDB.CEA$Gender)


```

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and hypercholesterolemia/LDL-levels group, as well as stratified by lipid-lowering drugs users.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per Hypercholesterolemia per Sex, ranked}

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaqua.LDLGroups.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Hypercholesterolemia.pdf"), plot = last_plot())

```

#### MCP1 plasma levels


```{r MCP1 per Hypercholesterolemia per Sex, ranked plasma}
# compare_means(MCP1_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)),
                  x = c("LDLGroup"),
                  y = "MCP1_plasma_olink_rank",
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.LDLGroup.pdf"), plot = last_plot())

# compare_means(MCP1_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)),
                  x = c("LDLGroup"),
                  y = "MCP1_plasma_olink_rank",
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Hypercholesterolemia.pdf"), plot = last_plot())

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per Hypercholesterolemia per Sex}

# ?ggpubr::ggboxplot()
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.LDLGroup.pdf"), plot = last_plot())

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Hypercholesterolemia.pdf"), plot = last_plot())
```

### MCP1 plasma levels


```{r MCP1 per Hypercholesterolemia per Sex, plasma}
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup)), 
                  x = c("LDLGroup"),
                  y = "MCP1", 
                  xlab = "LDL (mg/dL) per gender",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.LDLGroup.pdf"), plot = last_plot())

ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(LDLGroup) & !is.na(Med.Statin.LLD)), 
                  x = c("LDLGroup"),
                  y = "MCP1", 
                  xlab = "LDL (mg/dL) per LLD use",
                  ylab = "MCP1 plasma [pg/mL]",
                  color = "Med.Statin.LLD",
                  palette = c("#49A01D", "#1290D9"),
                  # add = "median_iqr")
                  add = c("median_iqr", "jitter"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Hypercholesterolemia.pdf"), plot = last_plot())

```


## Kidney function (eGFR)
We want to create figures of MCP1 levels stratified by kidney function. 

- Box and Whisker plot for MCP-1 plaque levels by chronic kidney disease (CKD) group (1, 2, 3, 4, 5)
- Box and Whisker plot for MCP-1 plasma levels by eGFR (MDRD-based) group (90+, 60-89, 30-59, <30)

```{r EGFR}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(eGFRGroup = factor(case_when(GFR_MDRD < 15 ~ "<15",
                                                             GFR_MDRD >= 15  & GFR_MDRD <= 29 ~ "15-29",
                                                             GFR_MDRD >= 30  & GFR_MDRD <= 59 ~ "30-59",
                                                             GFR_MDRD >= 60  & GFR_MDRD <= 89 ~ "60-89",
                                                             GFR_MDRD >= 90 ~ "90+")))

table(AEDB.CEA$eGFRGroup, AEDB.CEA$Gender)

table(AEDB.CEA$eGFRGroup, AEDB.CEA$KDOQI)

```

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and kidney function group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per EGFR per Sex, ranked}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.KDOQI.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.EGFR_KDOQI.pdf"), plot = last_plot())

```

#### MCP1 plasma levels


```{r MCP1 per EGFR per Sex, ranked plasma}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.EGFR.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.KDOQI.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.EGFR_KDOQI.pdf"), plot = last_plot())

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per EGFR per Sex}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.EGFR.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.KDOQI.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.EGFR_KDOQI.pdf"), plot = last_plot())

```

### MCP1 plasma levels

```{r MCP1 per EGFR per Sex, plasma}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_plasma_olink", 
                  xlab = "eGFR (mL/min per 1.73 m2) per gender",
                  ylab = "MCP1 plasma [AU]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.EGFR.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(KDOQI)), 
                  x = c("KDOQI"),
                  y = "MCP1_plasma_olink", 
                  xlab = "Kidney function (KDOQI) per gender",
                  ylab = "MCP1 plasma [AU]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1 + rotate_x_text(45), legend = "right") 
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.KDOQI.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(eGFRGroup) & !is.na(KDOQI)), 
                  x = c("eGFRGroup"),
                  y = "MCP1_plasma_olink", 
                  xlab = "eGFR (mL/min per 1.73 m2) by KDOQI group",
                  ylab = "MCP1 plasma [AU]",
                  color = "KDOQI",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.EGFR_KDOQI.pdf"), plot = last_plot())

```


## BMI
We want to create figures of MCP1 levels stratified by BMI. 

- Box and Whisker plot for MCP-1 plaque levels by BMI WHO group (underweight, normal, overweight, obese)
- Box and Whisker plot for MCP-1 plasma levels by BMI group (<18.5, 18.5-24.9, 25, 29.9, 30-24.9, 35+)

```{r BMI}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(BMIGroup = factor(case_when(BMI < 18.5 ~ "<18.5",
                                                     BMI >= 18.5  & BMI < 25 ~ "18.5-24",
                                                     BMI >= 25  & BMI < 30 ~ "25-29",
                                                     BMI >= 30  & BMI < 35 ~ "30-35",
                                                     BMI >= 35 ~ "35+"))) 

# require(labelled)
# AEDB.CEA$BMI_US <- as_factor(AEDB.CEA$BMI_US)
# AEDB.CEA$BMI_WHO <- as_factor(AEDB.CEA$BMI_WHO)
# table(AEDB.CEA$BMI_WHO, AEDB.CEA$BMI_US)

table(AEDB.CEA$BMIGroup, AEDB.CEA$Gender)
table(AEDB.CEA$BMIGroup, AEDB.CEA$BMI_WHO)

```

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per BMI per Sex, ranked}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI_byGender.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.BMI_byWHO.pdf"), plot = last_plot())

```

#### MCP1 plasma levels

> NOT AVAILABLE YET

```{r MCP1 per BMI per Sex, ranked plasma}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.BMI.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.BMI_byGender.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("AgeGroup"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.BMI_byWHO.pdf"), plot = last_plot())

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per BMI per Sex}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plaque [pg/ug]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.BMI.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.BMI_byGender.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("AgeGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.BMI_byWHO.pdf"), plot = last_plot())

```

### MCP1 plasma levels

```{r MCP1 per BMI per Sex, plasma}
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_plasma_olink", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plasma [AU]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.BMI.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup)), 
                  x = c("BMIGroup"),
                  y = "MCP1_plasma_olink", 
                  xlab = "BMI groups (kg/m2)",
                  ylab = "MCP1 plasma [AU]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.BMI_byGender.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(BMIGroup) & !is.na(BMI_WHO)), 
                  x = c("AgeGroup"),
                  y = "MCP1_plasma_olink", 
                  xlab = "BMI groups (kg/m2) per WHO categories",
                  ylab = "MCP1 plasma [AU]",
                  color = "BMI_WHO",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p1, legend = "right")
rm(p1)
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.BMI_byWHO.pdf"), plot = last_plot())

```


## Diabetes
We want to create figures of MCP1 levels stratified by type 2 diabetes. 

- Box and Whisker plot for MCP-1 plaque levels by type 2 diabetes group (no, yes)

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per Diabetes per Sex, ranked}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Diabetes status",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Diabetes.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Diabetes status per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Diabetes_byGender.pdf"), plot = last_plot())

```

#### MCP1 plasma levels

```{r MCP1 per Diabetes per Sex, ranked plasma}
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "Diabetes status",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Diabetes.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "Diabetes status per gender",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Diabetes_byGender.pdf"), plot = last_plot())

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per Diabetes per Sex}

# Global test
# compare_means(MCP1_pg_ug_2015 ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Diabetes status",
                  ylab = "MCP1 plaque [pg/ug]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = c("median_iqr", "jitter")) #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Diabetes.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015 ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Diabetes status per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Diabetes_byGender.pdf"), plot = last_plot())


```

### MCP1 plasma levels

```{r MCP1 per Diabetes per Sex, plasma}

# Global test
# compare_means(MCP1_pg_ug_2015 ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_plasma_olink", 
                  xlab = "Diabetes status",
                  ylab = "MCP1 plasma [AU]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = c("median_iqr", "jitter")) #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.rawDiabetes.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015 ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(DiabetesStatus)), 
                  x = c("DiabetesStatus"),
                  y = "MCP1_plasma_olink", 
                  xlab = "Diabetes status per gender",
                  ylab = "MCP1 plasma [AU]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Diabetes_byGender.pdf"), plot = last_plot())

```


## Smoking
We want to create figures of MCP1 levels stratified by smoking. 

- Box and Whisker plot for MCP-1 plaque levels by smoking group (never, ex, current)

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per Smoking per Sex, ranked}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Smoker status",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9", 
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Smoking.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Smoker status per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Smoking_byGender.pdf"), plot = last_plot())

```

#### MCP1 plasma levels

```{r MCP1 per Smoking per Sex, ranked plasma}
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "Smoker status",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9", 
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Smoking.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "Smoker status per gender",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Smoking_byGender.pdf"), plot = last_plot())

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per Smoking per Sex}

# Global test
# compare_means(MCP1_pg_ug_2015 ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Smoker status",
                  ylab = "MCP1 plaque [pg/ug]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9", 
                  add = c("median_iqr", "jitter")) #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Smoking.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015 ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Smoker status per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Smoking_byGender.pdf"), plot = last_plot())

```

### MCP1 plasma levels

```{r MCP1 per Smoking per Sex, plasma}
# Global test
# compare_means(MCP1_pg_ug_2015 ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_plasma_olink", 
                  xlab = "Smoker status",
                  ylab = "MCP1 plasma [AU]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9", 
                  add = c("median_iqr", "jitter")) #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Smoking.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015 ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(SmokerStatus)), 
                  x = c("SmokerStatus"),
                  y = "MCP1_plasma_olink", 
                  xlab = "Smoker status per gender",
                  ylab = "MCP1 plasma [AU]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = c("median_iqr", "jitter")) #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Smoking_byGender.pdf"), plot = last_plot())

```


## Stenosis
We want to create figures of MCP1 levels stratified by stenosis grade. 

- Box and Whisker plot for MCP-1 plaque levels by stenosis grade group (<70, 70-89, 90+)

```{r Stenosis}
library(dplyr)

AEDB.CEA <- AEDB.CEA %>% mutate(StenoticGroup = factor(case_when(stenose == "0-49%" ~ "<70",
                                                     stenose == "0-49%" ~ "<70",
                                                     stenose == "50-70%" ~ "<70",
                                                     stenose == "70-90%" ~ "70-89",
                                                     stenose == "50-99%" ~ "90+",
                                                     stenose == "70-99%" ~ "90+",
                                                     stenose == "100% (Occlusion)" ~ "90+",
                                                     stenose == "90-99%" ~ "90+")))

table(AEDB.CEA$StenoticGroup, AEDB.CEA$Gender)
table(AEDB.CEA$stenose, AEDB.CEA$StenoticGroup)

```

Now we can draw some graphs of plasma/plaque MCP1 levels per sex and age group.

### Inverse-rank transformed data

#### MCP1 plaque levels

```{r MCP1 per Stenosis per Sex, ranked}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Stenotic grade",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Stenosis.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Stenotic grade per gender",
                  ylab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.Stenosis_byGender.pdf"), plot = last_plot())

```

#### MCP1 plasma levels

```{r MCP1 per Stenosis per Sex, ranked plasma}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "Stenotic grade",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Stenosis.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "Stenotic grade per gender",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.Stenosis_byGender.pdf"), plot = last_plot())

```

### Raw data

Simalarly but now for the raw data as median ± interquartile range.

#### MCP1 plaque levels


```{r MCP1 per Stenosis per Sex}

# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Stenotic grade",
                  ylab = "MCP1 plaque [pg/ug]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Stenosis.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_pg_ug_2015", 
                  xlab = "Stenotic grade per gender",
                  ylab = "MCP1 plaque [pg/ug]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.raw.Stenosis_byGender.pdf"), plot = last_plot())

```

### MCP1 plasma levels

```{r MCP1 per Stenosis per Sex, plasma}
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_plasma_olink", 
                  xlab = "Stenotic grade",
                  ylab = "MCP1 plasma [AU]",
                  # color = "Gender",
                  # palette = c("#D5267B", "#1290D9"),
                  color = "#1290D9",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Stenosis.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
ggpubr::ggboxplot(AEDB.CEA %>% filter(!is.na(StenoticGroup)), 
                  x = c("StenoticGroup"),
                  y = "MCP1_plasma_olink", 
                  xlab = "Stenotic grade per gender",
                  ylab = "MCP1 plasma [AU]",
                  color = "Gender",
                  palette = c("#D5267B", "#1290D9"),
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.raw.Stenosis_byGender.pdf"), plot = last_plot())

```



## Circulating vs. plaque MCP1 levels

We will also make a nice correlation plot between plasma and plaque MCP1 levels.

```{r MCP1 correlations, plasma vs. plaque}

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015",
                  y = "MCP1_plasma_olink", 
                  xlab = "MCP1 plaque [pg/ug]",
                  ylab = "MCP1 plasma [AU]",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(8,750))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque_vs_plasma.raw.pdf"), plot = last_plot())

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_pg_ug_2015_rank",
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "MCP1 plaque [pg/ug]\n(inverse-rank transformation)",
                  ylab = "MCP1 plasma [AU]\n(inverse-rank transformation)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"), cor.coef.coord = c(2,3))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque_vs_plasma.rank.pdf"), plot = last_plot())

```

## Plaque vs. plaque MCP1 levels
We will also make a nice correlation plot between the two experiments of plaque MCP1 levels. 

```{r MCP1 correlations, plaque vs. plaque}
AEDB.CEA$MCP1_rank <- qnorm((rank(AEDB.CEA$MCP1, na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA$MCP1)))
summary(AEDB.CEA$MCP1)
summary(AEDB.CEA$MCP1_pg_ug_2015)

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1", 
                  y = "MCP1_pg_ug_2015",
                  xlab = "MCP1 plaque [pg/mL] (exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug] (exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque_vs_plaque.raw.pdf"), plot = last_plot())

ggpubr::ggscatter(AEDB.CEA, 
                  x = "MCP1_rank", 
                  y = "MCP1_pg_ug_2015_rank",
                  xlab = "MCP1 plaque [pg/mL]\n(INRT,  exp. no. 1)",
                  ylab = "MCP1 plaque [pg/ug]\n(INRT,  exp. no. 2)",
                  add = "reg.line", add.params = list(color = "#1290D9"),
                  conf.int = TRUE,
                  cor.coef = TRUE, cor.coeff.args = list(method = "spearman"))
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque_vs_plasma.rank.pdf"), plot = last_plot())

```


## Symptoms
We want to create per-symptom figures. 


```{r SymptomGroups}
library(dplyr)

table(AEDB.CEA$AgeGroup, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$Gender, AEDB.CEA$AsymptSympt2G)
table(AEDB.CEA$AsymptSympt2G)

```

Now we can draw some graphs of plasma/plaque MCP1 levels per symptom group.
```{r MCP1 per SymptomGroups}

# ?ggpubr::ggboxplot()
my_comparisons <- list(c("Asymptomatic", "Symptomatic"))

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "AsymptSympt2G", y = "MCP1_pg_ug_2015_rank",
                  title = "MCP1 plaque [pg/ug] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plaque [pg/ug]\n inverse-rank transformation",
                  color = "AsymptSympt2G", 
                  palette = c(uithof_color[16], uithof_color[23]),
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1, legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.AsymptSympt2G.pdf"), plot = last_plot())

rm(p1)

```

```{r MCP1 per SymptomGroups, plasma}
p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Symptoms.5G", y = "MCP1_plasma_olink_rank",
                  title = "MCP1 plasma [AU] levels per symptom", 
                  xlab = "Symptoms",
                  ylab = "MCP1 plasma [AU]\n inverse-rank transformation",
                  color = "Symptoms.5G", 
                  palette = "npg",
                  add = "dotplot", # Add dotplot
                  add.params = list(binwidth = 0.1, dotsize = 0.3)
          ) +
  stat_compare_means(comparisons = my_comparisons, method = "wilcox.test")
ggpar(p1 + rotate_x_text(45), legend = c("right"), legend.title = "Symptoms")

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plasma.AsymptSympt2G.pdf"), plot = last_plot())

rm(p1)

```

## Forest plots

We would also like to visualize the multivariable analyses results.
```{r load model data}
library(ggplot2)
library(openxlsx)
model1_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Uni.Protein.RANK.Symptoms.MODEL1.xlsx"))
model2_mcp1 <- read.xlsx(paste0(OUT_loc, "/", Today, ".AEDB.CEA.Bin.Multi.Protein.RANK.Symptoms.MODEL2.xlsx"))
model1_mcp1$model <- "univariate"
model2_mcp1$model <- "multivariate"

models_mcp1 <- rbind(model1_mcp1, model2_mcp1)
models_mcp1

```

Forest plot for experiment 2.

```{r forestplot plaque, experiment 2}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_pg_ug_2015_rank"]))

fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
  geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) + 
  geom_hline(yintercept = 1, lty = 2) +  # add a dotted line at x=1 after flip
  coord_flip(ylim = c(0.8, 1.7)) +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  ggtitle("Plaque MCP-1 levels (1 SD increment, exp. #2, n = 1190+)") +
  theme_minimal()  # use a white background
print(fp)

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.exp2.forest.pdf"), plot = fp)

rm(fp)
```

Forest plot for experiment 1.

```{r forestplot plaque, experiment 1}
dat <- data.frame(group = factor(c("Age, sex-adjusted", "Age, sex, and adjusted for risk factors"), 
                           
                           levels=c("Age, sex, and adjusted for risk factors", "Age, sex-adjusted")),
                  cen = c(models_mcp1$OR[models_mcp1$Predictor=="MCP1_rank"]),
                  low = c(models_mcp1$low95CI[models_mcp1$Predictor=="MCP1_rank"]),
                  high = c(models_mcp1$up95CI[models_mcp1$Predictor=="MCP1_rank"]))

fp <- ggplot(data = dat, aes(x = group, y = cen, ymin = low, ymax = high)) +
  geom_pointrange(linetype = 2, size = 1, colour = c("#1290D9", "#49A01D")) + 
  geom_hline(yintercept = 1, lty = 2) +  # add a dotted line at x=1 after flip
  coord_flip(ylim = c(0.8, 1.7)) +  # flip coordinates (puts labels on y axis)
  xlab("Model") + ylab("OR (95% CI) for symptomatic plaques") +
  ggtitle("Plaque MCP-1 levels (1 SD increment, exp. #1, n = 490+)") +
  theme_minimal()  # use a white background
print(fp)

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.exp1.forest.pdf"), plot = fp)

rm(fp)
```

## MCP1 vs. cytokines plaque levels correlations

We will plot the correlations of other cytokine plaque levels to the MCP1 plaque levels. These include:

- IL2
- IL4
- IL5
- IL6
- IL8
- IL9
- IL10
- IL12
- IL13
- IL21
- INFG
- TNFA
- MIF
- MCP1
- MIP1a
- RANTES
- MIG
- IP10
- Eotaxin1
- TARC
- PARC
- MDC
- OPG
- sICAM1
- VEGFA
- TGFB

In addition we will look at three metalloproteinases which were measured using an activity assay. 

- MMP2
- MMP8
- MMP9

The proteins were measured using FACS and LUMINEX. Given the different platforms used (FACS vs. LUMINEX), we will inverse rank-normalize these variables as well to scale them to the same scale as the MCP1 plaque levels.


We will set the measurements that yielded '0' to NA, as it is unlikely that any protein ever has exactly 0 copies. The '0' yielded during the experiment are due to the limits of the detection.

### Prepare data
```{r MCP1 vs Cytokines INRT}
cytokines <- c("IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", "IL13", "IL21", 
               "INFG", "TNFA", "MIF", "MCP1", "MIP1a", "RANTES", "MIG", "IP10", "Eotaxin1", 
               "TARC", "PARC", "MDC", "OPG", "sICAM1", "VEGFA", "TGFB")
metalloproteinases <- c("MMP2", "MMP8", "MMP9")

# fix names
names(AEDB.CEA)[names(AEDB.CEA) == "VEFGA"] <- "VEGFA"


proteins_of_interest <- c(cytokines, metalloproteinases)

proteins_of_interest_rank = unlist(lapply(proteins_of_interest, paste0, "_rank"))


# make variables numerics()
AEDB.CEA <- AEDB.CEA %>%
  mutate_each(funs(as.numeric), proteins_of_interest)
  
for(PROTEIN in 1:length(proteins_of_interest)){

  # UCORBIOGSAqc$Z <- NULL
  var.temp.rank = proteins_of_interest_rank[PROTEIN]
  var.temp = proteins_of_interest[PROTEIN]
  
  cat(paste0("\nSelecting ", var.temp, " and standardising: ", var.temp.rank,".\n"))
  cat(paste0("* changing ", var.temp, " to numeric.\n"))

  # AEDB.CEA <-  AEDB.CEA %>% mutate(AEDB.CEA[,var.temp] == replace(AEDB.CEA[,var.temp], AEDB.CEA[,var.temp]==0, NA))

  AEDB.CEA[,var.temp][AEDB.CEA[,var.temp]==0.000000]=NA

  cat(paste0("* standardising ", var.temp, 
             " (mean: ",round(mean(!is.na(AEDB.CEA[,var.temp])), digits = 6),
             ", n = ",sum(!is.na(AEDB.CEA[,var.temp])),").\n"))
  
  AEDB.CEA <- AEDB.CEA %>%
      mutate_at(vars(var.temp), 
        # list(Z = ~ (AEDB.CEA[,var.temp] - mean(AEDB.CEA[,var.temp], na.rm = TRUE))/sd(AEDB.CEA[,var.temp], na.rm = TRUE))
        list(RANK = ~ qnorm((rank(AEDB.CEA[,var.temp], na.last = "keep") - 0.5) / sum(!is.na(AEDB.CEA[,var.temp]))))
      )
  # str(UCORBIOGSAqc$Z)
  cat(paste0("* renaming RANK to ", var.temp.rank,".\n"))
  AEDB.CEA[,var.temp.rank] <- NULL
  names(AEDB.CEA)[names(AEDB.CEA) == "RANK"] <- var.temp.rank
}

# rm(var.temp, var.temp.rank)

```

### Visualize transformations

We will just visualize these transformations.

```{r MCP1 vs Cytokines Histograms}
proteins_of_interest_rank_mcp1 <- c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank", proteins_of_interest_rank)

proteins_of_interest_mcp1 <- c("MCP1_pg_ug_2015", "MCP1_pg_ml_2015", proteins_of_interest)

for(PROTEIN in proteins_of_interest_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "",
                    ggtheme = theme_minimal())
  print(p1)
  
}


for(PROTEIN in proteins_of_interest_rank_mcp1){
  cat(paste0("Plotting protein ", PROTEIN, ".\n"))
  
  p1 <- ggpubr::gghistogram(AEDB.CEA, PROTEIN,
                    # y = "..count..",
                    color = "white",
                    fill = "Gender",
                    palette = c("#1290D9", "#DB003F"),
                    add = "mean",
                    # rug = TRUE,
                    # add.params =  list(color = "black", linetype = 2),
                    title = paste0(PROTEIN, " plaque levels"),
                    xlab = "inverse-normal transformation",
                    ggtheme = theme_minimal())
  print(p1)
  
}
  
```

### Correlations

Here we calculate correlations between `MCP1_pg_ug_2015` and 28 other cytokines (including `MCP1` as measured in experiment 1. We use Spearman's test, thus, correlations a given in _rho_. Please note the indications of measurement methods:

- _L_: LUMINEX
- _E_: ELISA
- _a_: activity assay

```{r MCP1 vs Cytokines correlations}
# Installation of ggcorrplot()
# --------------------------------
if(!require(devtools)) 
  install.packages("devtools")
devtools::install_github("kassambara/ggcorrplot")

library(ggcorrplot)

# Creating matrix - inverse-rank transformation
# --------------------------------
AEDB.CEA.temp <- subset(AEDB.CEA, 
                          select = c(proteins_of_interest_rank_mcp1)
                                    )

# str(AEDB.CEA.temp)
AEDB.CEA.matrix.RANK <- as.matrix(AEDB.CEA.temp)
rm(AEDB.CEA.temp)

corr_biomarkers.rank <- round(cor(AEDB.CEA.matrix.RANK, 
                             use = "pairwise.complete.obs", #the correlation or covariance between each pair of variables is computed using all complete pairs of observations on those variables
                             method = "spearman"), 3)
# corr_biomarkers.rank

rename_proteins_of_interest_mcp1 <- c("MCP1 (L, exp2, pg/ug)", "MCP1 (L, exp2, pg/mL)", 
                                    "IL2", "IL4", "IL5", "IL6", "IL8", "IL9", "IL10", "IL12", 
                                    "IL13 (L)", "IL21 (L)", 
                                    "INFG", "TNFA", "MIF (L)", 
                                    "MCP1 (L, exp1)", "MIP1a (L)", "RANTES (L)", "MIG (L)", "IP10 (L)", 
                                    "Eotaxin1 (L)", "TARC (L)", "PARC (L)", "MDC (L)", 
                                    "OPG (L)", "sICAM1 (L)", "VEGFA (E)", "TGFB (E)", "MMP2 (a)", "MMP8 (a)", "MMP9 (a)")
colnames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)
rownames(corr_biomarkers.rank) <- c(rename_proteins_of_interest_mcp1)

corr_biomarkers_p.rank <- ggcorrplot::cor_pmat(AEDB.CEA.matrix.RANK, use = "pairwise.complete.obs", method = "spearman")

# ++++++++++++++++++++++++++++
# flattenCorrMatrix
# ++++++++++++++++++++++++++++
# cormat : matrix of the correlation coefficients
# pmat : matrix of the correlation p-values
flattenCorrMatrix <- function(cormat, pmat) {
  ut <- upper.tri(cormat)
  data.frame(
    row = rownames(cormat)[row(cormat)[ut]],
    column = rownames(cormat)[col(cormat)[ut]],
    cor  =(cormat)[ut],
    p = pmat[ut]
    )
}

corr_biomarkers.rank.df <- flattenCorrMatrix(corr_biomarkers.rank, corr_biomarkers_p.rank)


names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "row"] <- "Cytokine_X"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "column"] <- "CytokineY"
names(corr_biomarkers.rank.df)[names(corr_biomarkers.rank.df) == "cor"] <- "SpearmanRho"

DT::datatable(corr_biomarkers.rank.df)

fwrite(corr_biomarkers.rank.df, file = paste0(OUT_loc, "/",Today,".correlation_cytokines.txt"))

```

```{r MCP1 vs Cytokines heatmap}
# Add correlation coefficients
# --------------------------------
# argument lab = TRUE
p1 <- ggcorrplot(corr_biomarkers.rank, 
           method = "square", 
           type = "lower",
           title = "Cross biomarker correlations", 
           show.legend = TRUE, legend.title = bquote("Spearman's"~italic(rho)),
           ggtheme = ggplot2::theme_minimal, outline.color = "#FFFFFF",
           show.diag = TRUE,
           hc.order = FALSE, 
           lab = FALSE,
           digits = 3,
           tl.cex = 6,
           # xlab = c("MCP1"),
           # p.mat = corr_biomarkers_p.rank, sig.level = 0.05,
           colors = c("#1290D9", "#FFFFFF", "#E55738"))
p1
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.png"), plot = last_plot())
ggsave(filename = paste0(PLOT_loc, "/", Today, ".correlation_cytokines.pdf"), plot = last_plot())

rm(p1)

```

While visually actractive we are not necessarily interested in the correlations between all the cytokines, rather of MCP1 with other cytokines only.

```{r MCP1 vs Cytokines barplot}
temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2, pg/ug)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold
p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
          fill = "CytokineY",               # change fill color by cyl
          # color = "white",            # Set bar border colors to white
          palette = uithof_color,            # jco journal color palett. see ?ggpar
          xlab = "Cytokine",
          ylab = expression("Spearman's"~italic(rho)),
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 45, # Rotate vertically x axis texts
          cex = 0.8
          )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9)) 

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.barplot_pgug.MCP1_vs_Cytokines.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.barplot_pgug.MCP1_vs_Cytokines.pdf"), plot = last_plot())
rm(p1)

temp <- subset(corr_biomarkers.rank.df, Cytokine_X == "MCP1 (L, exp2, pg/mL)" )
temp$p_log10 <- -log10(temp$p)
p_threshold <- -log10(0.05/29)
p_threshold
p1 <- ggbarplot(temp, x = "CytokineY", y = "SpearmanRho",
          fill = "CytokineY",               # change fill color by cyl
          # color = "white",            # Set bar border colors to white
          palette = uithof_color,            # jco journal color palett. see ?ggpar
          xlab = "Cytokine",
          ylab = expression("Spearman's"~italic(rho)),
          sort.val = "desc",          # Sort the value in dscending order
          sort.by.groups = FALSE,     # Don't sort inside each group
          x.text.angle = 45, # Rotate vertically x axis texts
          cex = 0.8
          )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9)) 

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.barplot_pgmL.MCP1_vs_Cytokines.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.barplot_pgmL.MCP1_vs_Cytokines.pdf"), plot = last_plot())

rm(p1)

```

Another version - problably not good. 
```{r MCP1 vs Cytokines dotchart}

p1 <- ggdotchart(temp, x = "CytokineY", y = "p_log10",
           color = "CytokineY",                                # Color by groups
           palette = uithof_color, # Custom color palette
           xlab = "Cytokine",
           ylab = expression(log[10]~"("~italic(p)~")-value"),
           ylim = c(0, 6),
           sorting = "descending",                       # Sort value in descending order
           add = "segments",                             # Add segments from y = 0 to dots
           rotate = FALSE,                                # Rotate vertically
           # group = "CytokineY",                                # Order by groups
           dot.size = 8,                                 # Large dot size
           label = round(temp$SpearmanRho, digits = 3),                        # Add mpg values as dot labels
           font.label = list(color = "white", size = 8, 
                             vjust = 0.5)                   
           )
ggpar(p1, legend = "bottom", 
      legend.title = "") +
  theme(axis.text.x = element_text(size = 9),
        axis.text.y = element_text(size = 9))

ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.dotchart.MCP1_vs_Cytokines.png"), plot = last_plot())
ggsave(file = paste0(PLOT_loc, "/",Today,".AEDB.CEA.MCP1.plaque.dotchart.MCP1_vs_Cytokines.pdf"), plot = last_plot())

rm(temp, p1)

```


## MCP1 vs. cytokines plaque levels `lm()`

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines traits as a function of plasma/plaque MCP1 levels.
```{r CrossSec: Cytokines - linear regression MODEL1 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year, data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))

    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL1 RANK Writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Uni.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL1.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Uni.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```



### Model 2

In this model we correct for _Age_, _Gender_, _year of surgery_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of plaque cytokines as a function of plasma/plaque MCP1 levels.
```{r CrossSec: Cytokines - linear regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

GLM.results <- data.frame(matrix(NA, ncol = 15, nrow = 0))
cat("Running linear regression...\n")
for (protein in 1:length(TRAITS.PROTEIN.RANK)) {
  PROTEIN = TRAITS.PROTEIN.RANK[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  for (trait in 1:length(proteins_of_interest_rank)) {
    TRAIT = proteins_of_interest_rank[trait]
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.)))
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    ### univariate
    fit <- lm(currentDF[,PROTEIN] ~ currentDF[,TRAIT] + Age + Gender + ORdate_year + 
                Hypertension.composite + DiabetesStatus + SmokerStatus + 
                Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
                MedHx_CVD + stenose, 
              data = currentDF)
    model_step <- stepAIC(fit, direction = "both", trace = FALSE)
    print(model_step)
    print(summary(fit))
    
    GLM.results.TEMP <- data.frame(matrix(NA, ncol = 15, nrow = 0))
    GLM.results.TEMP[1,] = GLM.CON(fit, "AEDB.CEA", PROTEIN, TRAIT, verbose = TRUE)
    GLM.results = rbind(GLM.results, GLM.results.TEMP)
  }
}
cat("Edit the column names...\n")
colnames(GLM.results) = c("Dataset", "Predictor", "Trait",
                          "Beta", "s.e.m.",
                          "OR", "low95CI", "up95CI",
                          "T-value", "P-value", "r^2", "r^2_adj", "AE_N", "Model_N", "Perc_Miss")

cat("Correct the variable types...\n")
GLM.results$Beta <- as.numeric(GLM.results$Beta)
GLM.results$s.e.m. <- as.numeric(GLM.results$s.e.m.)
GLM.results$OR <- as.numeric(GLM.results$OR)
GLM.results$low95CI <- as.numeric(GLM.results$low95CI)
GLM.results$up95CI <- as.numeric(GLM.results$up95CI)
GLM.results$`T-value` <- as.numeric(GLM.results$`T-value`)
GLM.results$`P-value` <- as.numeric(GLM.results$`P-value`)
GLM.results$`r^2` <- as.numeric(GLM.results$`r^2`)
GLM.results$`r^2_adj` <- as.numeric(GLM.results$`r^2_adj`)
GLM.results$`AE_N` <- as.numeric(GLM.results$`AE_N`)
GLM.results$`Model_N` <- as.numeric(GLM.results$`Model_N`)
GLM.results$`Perc_Miss` <- as.numeric(GLM.results$`Perc_Miss`)

```

```{r CrossSec: Cytokines - linear regression MODEL2 RANK, writing}
DT::datatable(GLM.results)

# Save the data
cat("Writing results to Excel-file...\n")
### Univariate
library(openxlsx)
write.xlsx(GLM.results,
           file = paste0(OUT_loc, "/",Today,".AEDB.CEA.Con.Multi.MCP1_Plaque.Cytokines_Plaques.RANK.MODEL2.xlsx"),
           row.names = FALSE, col.names = TRUE, sheetName = "Con.Multi.PlaquePheno")
# Removing intermediates
cat("Removing intermediate files...\n")
rm(TRAIT, trait, currentDF, GLM.results, GLM.results.TEMP, fit, model_step)


```

## MCP1 levels vs. vulnerability index

Here we calculate the plaque instability/vulnerability index and visualize the MCP1 levels in plaque and plasma.

```{r Plaque Vulnerability}
# Plaque vulnerability

table(AEDB.CEA$Macrophages.bin)
table(AEDB.CEA$Fat.bin_10)
table(AEDB.CEA$Collagen.bin)
table(AEDB.CEA$SMC.bin)
table(AEDB.CEA$IPH.bin)

# SPSS code

# 
# *** syntax- Plaque vulnerability**.
# COMPUTE Macro_instab = -999.
# IF macrophages.bin=2 Macro_instab=1.
# IF macrophages.bin=1 Macro_instab=0.
# EXECUTE.
# 
# COMPUTE Fat10_instab = -999.
# IF Fat.bin_10=2 Fat10_instab=1.
# IF Fat.bin_10=1 Fat10_instab=0.
# EXECUTE.
# 
# COMPUTE coll_instab=-999.
# IF Collagen.bin=2 coll_instab=0.
# IF Collagen.bin=1 coll_instab=1.
# EXECUTE.
# 
# 
# COMPUTE SMC_instab=-999.
# IF SMC.bin=2 SMC_instab=0.
# IF SMC.bin=1 SMC_instab=1.
# EXECUTE.
# 
# COMPUTE IPH_instab=-999.
# IF IPH.bin=0 IPH_instab=0.
# IF IPH.bin=1 IPH_instab=1.
# EXECUTE.
# 
# COMPUTE Instability=Macro_instab + Fat10_instab +  coll_instab + SMC_instab + IPH_instab.
# EXECUTE.

# Fix plaquephenotypes
attach(AEDB.CEA)
# mac instability
AEDB.CEA[,"MAC_Instability"] <- NA
AEDB.CEA$MAC_Instability[Macrophages.bin == -999] <- NA
AEDB.CEA$MAC_Instability[Macrophages.bin == "no/minor"] <- 0
AEDB.CEA$MAC_Instability[Macrophages.bin == "moderate/heavy"] <- 1

# fat instability
AEDB.CEA[,"FAT10_Instability"] <- NA
AEDB.CEA$FAT10_Instability[Fat.bin_10 == -999] <- NA
AEDB.CEA$FAT10_Instability[Fat.bin_10 == " <10%"] <- 0
AEDB.CEA$FAT10_Instability[Fat.bin_10 == " >10%"] <- 1

# col instability 
AEDB.CEA[,"COL_Instability"] <- NA
AEDB.CEA$COL_Instability[Collagen.bin == -999] <- NA
AEDB.CEA$COL_Instability[Collagen.bin == "no/minor"] <- 1
AEDB.CEA$COL_Instability[Collagen.bin == "moderate/heavy"] <- 0

# smc instability
AEDB.CEA[,"SMC_Instability"] <- NA
AEDB.CEA$SMC_Instability[SMC.bin == -999] <- NA
AEDB.CEA$SMC_Instability[SMC.bin == "no/minor"] <- 1
AEDB.CEA$SMC_Instability[SMC.bin == "moderate/heavy"] <- 0

# iph instability
AEDB.CEA[,"IPH_Instability"] <- NA
AEDB.CEA$IPH_Instability[IPH.bin == -999] <- NA
AEDB.CEA$IPH_Instability[IPH.bin == "no"] <- 0
AEDB.CEA$IPH_Instability[IPH.bin == "yes"] <- 1

detach(AEDB.CEA)

table(AEDB.CEA$MAC_Instability, useNA = "ifany")
table(AEDB.CEA$FAT10_Instability, useNA = "ifany")
table(AEDB.CEA$COL_Instability, useNA = "ifany")
table(AEDB.CEA$SMC_Instability, useNA = "ifany")
table(AEDB.CEA$IPH_Instability, useNA = "ifany")

# creating vulnerability index
AEDB.CEA <- AEDB.CEA %>% mutate(Plaque_Vulnerability_Index = factor(rowSums(.[grep("_Instability", names(.))], na.rm = TRUE)),
                                )

table(AEDB.CEA$Plaque_Vulnerability_Index, useNA = "ifany")

# str(AEDB.CEA$Plaque_Vulnerability_Index)

```

Here we plot the levels of inverse-rank normal transformed `MCP1` plaque levels from experiment 1 and 2 to the `Plaque vulnerability index`. 
```{r Fix ORyearGroup}
library(sjlabelled)

attach(AEDB.CEA)
AEDB.CEA$yeartemp <- as.numeric(year(AEDB.CEA$dateok))
AEDB.CEA[,"ORyearGroup"] <- NA
AEDB.CEA$ORyearGroup[yeartemp <= 2007] <- "< 2007"
AEDB.CEA$ORyearGroup[yeartemp > 2007] <- "> 2007"
detach(AEDB.CEA)

table(AEDB.CEA$ORyearGroup, AEDB.CEA$ORdate_year)
```

### MCP1 plaque levels vs. plaque vulnerability index

```{r MCP1 per PlaqueVulnerabilityIndex}
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/ug]\n(INT, exp 2)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgug.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgmL.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p3 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p3, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp1_pgmL.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_pg_ug_2015_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/ug]\n(INT, exp 2)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgug.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p2 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_pg_ml_2015_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 2)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p2, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp2_pgmL.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())

# compare_means(MCP1_pg_ug_2015_rank ~ AgeGroup,  data = AEDB.CEA, method = "kruskal.test")
p3 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plaque [pg/mL]\n(INT, exp 1)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") #+
  # stat_compare_means(method = "kruskal.test")
ggpar(p3, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plaque.exp1_pgmL.PlaqueVulnerabilityIndex_Facet_byYear.pdf"), plot = last_plot())

```

### MCP1 plasma levels vs. plaque vulnerability index

```{r MCP1 per PlaqueVulnerabilityIndex Plasma}
# Global test
# compare_means(MCP1_pg_ug_2015_rank ~ Gender,  data = AEDB.CEA, method = "wilcox.test")
p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plasma [AU]\n(inverse-normal transformation)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plasma.PlaqueVulnerabilityIndex.pdf"), plot = last_plot())

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plasma [AU]\n(inverse-normal transformation)",
                  color = "Plaque_Vulnerability_Index",
                  palette = "npg",
                  facet.by = "ORyearGroup",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plasma.PlaqueVulnerabilityIndex_FacetbyYear.pdf"), plot = last_plot())

p1 <- ggpubr::ggboxplot(AEDB.CEA, 
                  x = "Plaque_Vulnerability_Index",
                  y = "MCP1_plasma_olink_rank", 
                  xlab = "Plaque vulnerability index",
                  ylab = "MCP1 plasma [AU]\n(inverse-normal transformation)",
                  color = "ORyearGroup",
                  palette = "npg",
                  add = "jitter") #+
  # stat_compare_means(method = "wilcox.test")
ggpar(p1, legend = "bottom", legend.title = "Plaque vulnerability index")
ggsave(filename = paste0(PLOT_loc, "/", Today, ".MCP1.plasma.PlaqueVulnerabilityIndex_byYear.pdf"), plot = last_plot())

rm(p1)

```

### Model 1

In this model we correct for _Age_, _Gender_, and _year of surgery_.

Here we use the inverse-rank normalized data - visually this is more normally distributed.

Analysis of the plaque vulnerability indez as a function of plasma/plaque MCP1 levels.
```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL1 RANK, include=TRUE, paged.print=TRUE}
TRAITS.PROTEIN.RANK.extra = c("MCP1_pg_ug_2015_rank", "MCP1_pg_ml_2015_rank",  "MCP1_rank", "MCP1_plasma_olink_rank")

GLM.results <- data.frame(matrix(NA, ncol = 16, nrow = 0))
for (protein in 1:length(TRAITS.PROTEIN.RANK.extra)) {
  PROTEIN = TRAITS.PROTEIN.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M1, ORdate_epoch) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    # table(currentDF$ORdate_year)
    ### univariate
     # + Hypertension.composite + DiabetesStatus + SmokerCurrent + 
     #            Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + 
     #            CAD_history + Stroke_history + Peripheral.interv + stenose
    fit <- polr(currentDF[,TRAIT] ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year, 
              data  =  currentDF, 
              Hess = TRUE)
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }


```

### Model 2

In this model we correct for _Age_, _Gender_, _Hypertension status_, _Diabetes status_, _current smoker status_, _lipid-lowering drugs (LLDs)_, _antiplatelet medication_, _eGFR (MDRD)_, _BMI_, _MedHx_CVD_ (combination of _CAD history_, _stroke history_, and _peripheral interventions_), and _stenosis._.


```{r CrossSec: Plaque_Vulnerability_Index - ordinal regression MODEL2 RANK, include=TRUE, paged.print=TRUE}

for (protein in 1:length(TRAITS.PROTEIN.RANK.extra)) {
  PROTEIN = TRAITS.PROTEIN.RANK.extra[protein]
  cat(paste0("\nAnalysis of ",PROTEIN,".\n"))
  TRAIT = "Plaque_Vulnerability_Index"
    cat(paste0("\n- processing ",TRAIT,"\n\n"))
    currentDF <- as.data.frame(AEDB.CEA %>%
      dplyr::select(., PROTEIN, TRAIT, COVARIATES_M2) %>%
      filter(complete.cases(.))) %>%
      filter_if(~is.numeric(.), all_vars(!is.infinite(.))) %>%
      droplevels(.)
    
    # fix numeric OR year
    currentDF$ORdate_year <- as.numeric(currentDF$ORdate_year)
    
    # for debug
    # print(DT::datatable(currentDF))
    # print(nrow(currentDF))
    # print(str(currentDF))
    # print(class(currentDF[,TRAIT]))
    ### univariate

    fit <- polr(as.factor(currentDF[,TRAIT]) ~ currentDF[,PROTEIN] + Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose,
              data  =  currentDF,
              Hess = TRUE)
    
    print(summary(fit))
    
    ## store table
    (ctable <- coef(summary(fit)))

    ## calculate and store p values
    p <- pnorm(abs(ctable[, "t value"]), lower.tail = FALSE) * 2
    
    ## combined table
    print((ctable <- cbind(ctable, "p value" = p)))
  }

```

## Comparison sample types OLINK-platform

We performed a pilot experiment comparing plasma and plaque-derived protein levels as measured using the OLINK platform.

My colleague, Arjan Boltjes, analyzed this. Below some graphs and some statistics. 

_estimate_:    -0.0004093809
_statistic_:   -0.003774306
_p.value_:     0.9969974
_parameter_:   85
_conf.low_:    -0.2110394
_conf.high_:   0.210257	
_method_:      Pearson's product-moment correlation	
_alternative:_ two.sided

**Figure 1: Distributions of plaque and plasma MCP1 levels.** Measured using the OLINK-platform (CVD-III panel). Pilot experiment with n = 88 samples. 
![Distributions plaque and plasma MCP1 levels](plasma_vs_bulk_OLINK_MCP1/figures/hist_olink-2.png)

**Figure 2: Comparison of plaque and plasma MCP1 levels.** Measured using the OLINK-platform (CVD-III panel). Pilot experiment with n = 88 samples. _AU_ = Arbitrary unit.
![Comparing plaque and plasma MCP1 levels (AU)](plasma_vs_bulk_OLINK_MCP1/figures/corr_olink-1.png)


# Session information

------

    Version:      v1.0.13
    Last update:  2020-07-07
    Written by:   Sander W. van der Laan (s.w.vanderlaan-2[at]umcutrecht.nl).
    Description:  Script to analyse MCP1 from the Ather-Express Biobank Study.
    Minimum requirements: R version 3.5.2 (2018-12-20) -- 'Eggshell Igloo', macOS Mojave (10.14.2).
    
    **MoSCoW To-Do List**
    The things we Must, Should, Could, and Would have given the time we have.
    _M_
    * DONE - analysis on plasma based on OLINK platform
    * DONE - analysis on the pilot dataset on the OLINK platform, comparing plasma vs. plaque
    * DONE - linear regression models (model 1 and model 2) of `MCP1_pg_ug_2015` with cytokines
    * DONE - check out the difference between the measuremens of `MCP1` and `MCP1_pg_ug_2015` > `MCP1_pg_ug_2015` and `MCP1_pg_ml_2015` give similar results, `MCP1_pg_ug_2015` is more correct as this is corrected for the total amount of protein in the protein-sample used for the measurement. 
    * DONE - double check the plotting of the MACE
    * DONE - add the statistics for the correlation of `MCP1_pg_ug_2015` with the cytokines
    * DONE - add the comparison between `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`
    * DONE - analysis in the context of year of surgery given Van Lammeren _et al._ 
    * DONE - add analysis on vulnerability index
    * DONE - add analysis on binary and ordinal plaque phenotypes
    * DONE - add boxplots of MCP1 levels stratified by confounders/variables
    
    _S_
    * DONE prettify forest plot
    
    _C_
    
    
    _W_
    
    
    **Changes log**
    * v1.0.14 Add analysis on plasma based MCP1 levels measured through OLINK, n ± 700, limited to symptomatic patients only.
    * v1.0.13 Splitting RMDs into plaque-focused, and one including plasma levels of MCP1.
    * v1.0.12 Add boxplots of MCP1 levels stratified by confounder/variables.
    * v1.0.11 Add analysis of pilot data comparing OLINK-platform based MCP1 levels in plasma and plaque.
    * v1.0.10 Add analyses for all three `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`. Add comparison between `MCP1`, `MCP1_pg_ml_2015`, and `MCP1_pg_ug_2015`. Add (and fixed) ordinal regression. Double checked which measurement to use. 
    * v1.0.9 Added linear regression models for MCP1 vs. cytokines plaque levels. Double checked upload of MACE-plots. Added statistics from correlation (heatmap) to txt-file.
    * v1.0.8 Fixed error in MCP1 plasma analysis. It turns out the `MCP1` and `MCP1_pg_ug_2015` variables are _both_ measured in plaque, in two separate experiments, exp. no. 1 and exp. no. 2, respectively. 
    * v1.0.7 Fixed the per Age-group MCP1 Box plots. Added correlations with other cytokines in plaques.
    * v1.0.6 Only analyses and figures that end up in the final manuscript.
    * v1.0.5 Update with 30- and 90-days survival.
    * v1.0.4 Updated with Cox-regressions.
    * v1.0.3 Included more models.
    * v1.0.2 Bugs fixed.
    * v1.0.1 Extended with linear and logistic regressions.
    * v1.0.0 Inital version.
    

------

```{r eval = TRUE}
sessionInfo()
```

# Saving environment
```{r Saving}
save.image(paste0(PROJECT_loc, "/",Today,".",PROJECTNAME,".sample_selection.RData"))
```

------
<sup>&copy; 1979-2020 Sander W. van der Laan | s.w.vanderlaan-2[at]umcutrecht.nl | [swvanderlaan.github.io](https://swvanderlaan.github.io).</sup>
------

